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Google DeepMind’s latest work on Gemini 2.0 for robotics shows a remarkable shift in how large multimodal AI models are used to drive real-world automation. Instead of training robots in isolation for specific tasks, DeepMind introduced two specialized models: Gemini Robotics: a vision-language-action (VLA) model built on Gemini 2.0. It accepts  physical actions as a new output modality for directly controlling robots. Gemini Robotics-ER: a version of Gemini that incorporates embodied reasoning (ER) and spatial understanding. It allows roboticists to run their own programs along with Gemini’s spatial reasoning capabilities. This is monumental because Google demonstrates how you can take a multimodal artificial intelligence model, fine-tune it and apply it for robotics. Since it is multimodal, the robotic systems learn to generalize better rather than being proficient at a particular task without needing massive amounts of data to add a new ability. In this blog we will go through the key findings of the Gemini Robotics, the architecture, training pipeline and discuss the new capabilities it unlocks.  Why Traditional Robotics Struggle? Training robots has always been an expensive and complex task. Most of the robots are trained with supervised datasets, reinforcement learning or imitation learning, but each approach has significant limitations. Supervised learning: needs massive annotated datasets. This makes scaling difficult. Reinforcement learning (RL): It has only been proven effective in controlled environments. It still needs millions of trial and error interactions and still fails to generalize to the real-world applications. Imitation learning (IL): It is efficient but it needs large scale expert demonstrations. It can be difficult to find demonstrations for each and every scenario. These challenges lead to narrowly specialized models that work well in training environments but break down in real-world settings. A warehouse robot trained to move predefined objects might struggle if an unexpected item appears. A navigation system trained in simulated environments might fail in new locations with different lighting, obstacles, or floor textures.  Hence, the core issue of traditional robots is the lack of true generalization. However, DeepMind’s Gemini Robotics presents a solution to this problem by rethinking how robots are trained and how they interact with their environments.  What Makes Gemini Robotics Different? Gemini Robotics is a general-purpose model capable of solving dexterous tasks in different environments and supports different robot embodiments. It uses Gemini 2.0 as a foundation and extends the multimodal capabilities to not only understand tasks through vision and language but also to act autonomously in the physical world. The integration of physical actions as a new output modality, alongside vision and language processing, allow the model to control robots directly. It helps the robots to adapt and perform complex tasks with minimal human interventions. Source Architecture Overview Gemini Robotics is built around an advanced vision-language-action model (VLA), where vision and language inputs are integrated with robotic control outputs. The core idea behind this is to help the model to perceive its environment, understand natural language instructions and act in the real-world task by controlling the robot’s actions.  It is a transformer based architecture. The key components include: Vision Encoder: This module processes visual inputs from cameras or sensors, extracting spatial and object-related information. The encoder is capable of recognizing objects, detecting their positions, and understanding environmental contexts in dynamic settings. Language Encoder: The language model interprets natural language instructions. It converts user commands into an internal representation that can be translated into actions by the robot. The strength of Gemini Robotics lies in its ability to comprehend ambiguous language, contextual nuances, and even tasks with incomplete information. Action Decoder: The action decoder translates the multimodal understanding of the environment into actionable robotic movements. These include tasks like navigation, object manipulation, and interaction with external tools. Training Pipeline The training of these models is also unique as it combines multiple data sources and tasks to ensure that the model is good at generalizing across different settings.  Data Collection The training process begins with collecting a diverse range of data from robotic simulations and real-world environments. This data includes both visual data such as images, videos, depth maps, and sensor data, and linguistic data such as task descriptions, commands, and natural language instructions. To create a robust dataset, DeepMind uses a combination of both synthetic data from controlled environments and real-world data captured from real robots performing tasks. Pretraining The model is first pretrained on multimodal datasets, where it learns to associate vision and language patterns with tasks. This phase is designed to give the model an understanding of fundamental object recognition, navigation, and task execution in various contexts. Pretraining helps the model learn generalizable representations of tasks without having to start from scratch for each new environment. Fine-tuning on Robotic Tasks After pretraining, the model undergoes fine-tuning using real-world robotic data to improve its task-specific capabilities. Here, the model is exposed to a wide range of tasks from simple object manipulation to complex multi-step actions in dynamic environments. Fine-tuning is done using a combination of supervised learning for task labeling and reinforcement learning for optimizing robotic behaviors through trial and error. Reinforcement Learning for Real-World Adaptation A key component of the Gemini Robotics pipeline is the use of reinforcement learning (RL), especially in the fine-tuning stage. Through RL, the robot learns by performing actions and receiving feedback based on the success or failure of the task. This allows the model to improve over time and develop an efficient policy for action selection. RL also helps the robot generalize its learned actions to different real-world environments. Embodied Reasoning and Continuous Learning The model is also designed for embodied reasoning, which allows it to adjust its actions based on ongoing environmental feedback. This means that Gemini Robotics is not limited to a static training phase but is capable of learning from new experiences as it interacts with its environment. This continuous learning process is crucial for ensuring that the robot remains adaptable, capable of refining its understanding and improving its behavior after deployment. Gemini Robotics-ER Building on the capabilities of Gemini Robotics, this model introduces embodied reasoning (ER). What is Embodied Reasoning? Embodied reasoning refers to the ability of the model to understand and plan based on the physical space it occupies. Unlike traditional models that react to sensory input or follow pre-programmed actions, Gemini Robotics-ER has a built-in capability to understand spatial relationships and reason about movement.  Source This enables the robot to assess its environment more holistically, allowing for smarter decisions about how it should approach tasks like navigation, object manipulation, or avoidance of obstacles. For example, a robot with embodied reasoning wouldn’t just move toward an object based on visual recognition. Instead, it would take into account factors like: Spatial context: Is the object within reach, or is there an obstacle blocking the way? Task context: Does the object need to be lifted, moved to another location, or simply avoided? Environmental context: What other objects are nearby, and how do they affect the task at hand? Source Gemini 2.0’s Embodied Reasoning Capabilities The Gemini 2.0 model already provided embodied reasoning capabilities which are further improved in the Gemini Robotics-ER model. It needs no additional robot-specific data or training as well. Some of the capabilities include: Object Detection: It can perform open-world 2D object detection, and generate accurate bounding boxes for objects based on explicit and implicit queries. Pointing: The model can point to objects, object parts, and spatial concepts like where to grasp or place items based on natural language descriptions. Trajectory Prediction: Using its pointing capabilities, Gemini 2.0 predicts 2D motion trajectories grounded in physical observations, enabling the robot to plan movement. Grasp Prediction: Gemini Robotics-ER extends this by predicting top-down grasps for objects, enhancing interaction with the environment. Multi-View Correspondence: Gemini 2.0 processes stereo images to understand 3D scenes and predict 2D point correspondences across multiple views. Example of 2D trajectory prediction. Source How Gemini Robotics-ER Works? Gemini Robotics-ER incorporates several key innovations in its architecture to facilitate embodied reasoning. Spatial mapping and modeling This helps the robot to build and continuously update a 3D model of its surroundings. This spatial model allows the system to track both static and dynamic objects, as well as the robot's own position within the environment. Multimodal fusion It combines vision sensors, depth cameras, and possibly other sensors (e.g., LiDAR).  Spatial reasoning algorithms These algorithms help the model predict interactions with environmental elements. Gemini Robotics-ER’s task planner integrates spatial understanding, allowing it to plan actions based on real-world complexities. Unlike traditional models, which follow predefined actions, Gemini Robotics-ER can plan ahead for tasks like navigating crowded areas, manipulating objects, or managing task sequences (e.g., stacking objects). ERQA (Embodied Reasoning Quality Assurance) It is an open-source benchmark to evaluate embodied reasoning capabilities of multimodal models. In the fine-tuned Gemini models it acts as a feedback loop which evaluates the quality and accuracy of spatial reasoning, decision-making, and action execution in real-time. ERQA Question categories. Source The core of ERQA is its ability to evaluate whether the robot's actions are aligned with its planned sequence and expected outcomes based on the environment’s current state. In practice, ERQA ensures that the robot: Accurately interprets spatial relationships between objects and obstacles in its environment. Adapts to real-time changes in the environment, such as moving obstacles or shifts in spatial layout. Executes complex actions like object manipulation or navigation without violating physical constraints or failing to complete tasks. The system generates feedback signals that inform the model about the success or failure of its decisions. These signals are used for real-time correction, ensuring that errors in spatial understanding or action execution are swiftly addressed and corrected. Why Do These Models Matter for Robotics? One of the biggest breakthroughs in Gemini Robotics is its ability to unify perception, reasoning, and control into a single AI system. Instead of relying solely on robotic experience, Gemini leverages vast external knowledge from videos, images, and text, enabling robots to make more informed decisions. For example, if a household robot encounters a new appliance it has never seen before, a traditional model would likely fail unless it had been explicitly trained on that device. In contrast, Gemini can infer the appliance's function based on prior knowledge from images and instructional text it encountered during pretraining. This ability to extrapolate and reason about unseen scenarios is what makes multimodal AI so powerful for robotics. Through this approach, DeepMind is laying the foundation for more intelligent and adaptable humanoid robots capable of operating across a wide range of industries from warehouse automation to household assistance and beyond. Conclusion In short, Google introduces models and benchmarks and shows how robots can do more and adapt more to different situations. By being general, interactive, and dexterous, it can handle a variety of tasks, respond quickly to changes, and perform actions with precision, much like humans.  📘 Download our newest e-book, The rise of intelligent machines to learn more about implementing physical AI models.

Imagine a world where in the morning the sun rises over busy cities not just with human activity but also with the intelligent machines moving around. A world where your morning coffee is brewed by a robot that not only knows your exact taste preferences but also navigates a kitchen with human-like grace. In this world autonomous delivery drones or robots navigate the urban maze and deliver fresh groceries, essential medicines, and even lunch orders directly to your doorstep. There would also be intelligent robots and drones inspecting cities and assisting in traffic management, taking charge of urban maintenance.  Hospitals would have AI-powered robots efficiently deliver medications to patients and  warehouses would have  robots sort, pack, and ship orders.  This is no longer a science fiction story, it is the emerging reality of Physical AI.  Physical AI illustration by ArchetypeAI (Source) As projected in the article Nvidia could get a bionic boost from the rise of the robots, Physical AI is the next frontier of artificial intelligence. It is suggested that by 2035, there could be as many as 1.3 billion AI-powered robots operating across the globe. In manufacturing alone, the integration of Physical AI could unlock a multi trillion-dollar market, while advancements in healthcare and transportation promise to dramatically improve safety and efficiency. These statistics underline the enormous potential as well as requirement to harness Physical AI for practical, real-world applications. Jensen Huang speaking about humanoids during the 2025 CES event (Source) In this blog, we will deep dive into the world of Physical AI. We'll explore what it is, how it is different from other forms of AI like embodied AI. We will also discuss the data and hardware challenges that need to be overcome and discuss the importance of AI alignment in creating safe systems. We will also explore the role of Encord in Physical AI.  What is Physical AI? Physical AI refers to the integration of AI (exists in software form), with physical systems. Physical AI enables machines to interact with and adapt to the real world. It combines AI algorithms, such as machine learning, computer vision, and natural language processing, with robotics, sensors, and actuators to create systems that can perceive, reason, and act in physical environments. Block diagram of the Newton Physical AI foundation model (Source) Key Characteristics of Physical AI Following are the key characters of Physical AI. Embodiment: Physical AI systems are embodied in physical forms, such as robots, drones, or autonomous vehicles which allow it to interact directly with its surroundings. Perception: Physical AI systems make use of sensors (e.g., cameras, microphones, LiDAR) to gather data about their environment. Decision-Making: AI algorithms in Physical AI systems process sensor data to make decisions or predictions. Action: Actuators (e.g., motors, arms, wheels) enable these systems to perform physical tasks, such as moving, grasping, or manipulating objects. Adaptability: Physical AI systems can learn and adapt to new situations or environments over time. Components of Physical AI System Physical AI systems integrate hardware, software, and connectivity to enable intelligent interaction with the physical world. The following are the core components: Sensors Sensors allow Physical AI systems to see and feel  their environment. Sensors help it to collect real-time data enabling the system to understand and respond to external conditions. It can use one or more of the following sensors to understand its surroundings. Cameras: It is used for computer vision tasks. Cameras capture visual information and allow the system to recognize objects, track movements, and interpret visual cues. LiDAR/Radar: These sensors emit signals and measure their reflections to create detailed 3D maps of surroundings. These sensors are essential for navigation. Microphones: It helps capture audio data, enabling the system to process sounds for voice recognition. Inertial Measurement Units (IMUs): It comprises accelerometers and gyroscopes to track motion, orientation, and acceleration. It also helps in stabilizing the physical body of Physical AI  systems. Temperature, Pressure, or Proximity Sensors: These sensors monitor environmental factors such as heat, force, or distance to nearby objects and allow the Physical AI system to react appropriately to changes. Actuators Actuators are responsible for executing physical actions based on the decisions taken by the system in order to enable interaction with the environment. For example, if a robot sees an apple through a camera and receives instruction to pick it up through a microphone, it uses different motors in its arm to plan a path to pick it up. Following are some actuator devices: Motors: Drive components like wheels or robotic arms assists the movement and manipulation of objects. Servos: Provide precise control over angular or linear positions which are crucial for tasks requiring exact movements. Hydraulic/Pneumatic Systems: It uses fluid or air pressure to generate powerful movements and are used in heavy machines or robotic systems requiring significant force. Speakers: It converts electrical signals into sound to provide audio feedback or communicate with users. AI Processing Units The AI processing units handle the intensive computations required for processing sensor data and running AI algorithms to make real-time decisions. Some examples are following: Graphics Processing Units (GPUs): Specialized for parallel processing, GPUs accelerate tasks like image and signal processing which are essential for real-time AI applications. Tensor Processing Units (TPUs): Custom-developed by Google, TPUs are designed to efficiently handle machine learning workloads, particularly for neural network computations. Edge Computing Devices: These processors enable data processing at the source (i.e., on the device itself), reducing latency and reliance on cloud connectivity, which is vital for time-sensitive applications. NVIDIA Jetson Orin Nano Dk for Edge AI (Source) Mechanical Hardware It is the physical components that provide structure to Physical AI and facilitate movement. It provides the tangible interface between the AI system and its environment. The following are some of the examples: Chassis/Frames: It provides foundational structures to robots, drones, or vehicles and supports all other components of the system. Articulated Limbs: These are the robotic arms or legs that have multiple joints to allow movements and the ability to perform complex tasks. Grippers/Manipulators: These are the end-effectors designed to grasp, hold, or manipulate objects. It enables the system to interact physically with various items. MIRAI AI Enabled Robotic ARM from KUKA (Source) AI Software & Algorithms This is the brain of the Physical AI system. It processes the sensor data and helps in making decisions. The key software for Physical AI are as follows. Machine Learning Models: It is one of the most important parts of Physical AI as it helps the system to understand its environment. It enables systems to learn optimal actions through trial and error. Robot Operating System (ROS): ROS is the open-source robotics middleware. It is a framework that provides a collection of software libraries and tools to build robot applications and enables hardware abstraction and device control. Control Systems The control system translates the decision from AI Software and Algorithm into commands which are executed by actuators. Following are the important control systems: PID Controllers: PID controller uses proportional, Integral, and derivative calculations for the system outputs such that required for the motion control. Real-Time Operating Systems (RTOS): RTOS manages hardware resources and ensures real-time execution of tasks. This is very important in Physical AI systems which require precise timing. Can AI Have a Physical Form? When most people imagine AI, they think of it as some application, computer programs, or invisible systems like Netflix suggesting a show, Siri answering questions, or chatbots like ChatGPT answering queries. This kind of AI lives entirely in the digital world and works behind the scenes like a ghost that thinks and calculates but it can not move around us and touch and interact with the physical world. In this application, the ai is a software system, like a brain without a body. Physical AI flips this idea. Instead of being trapped in a computer's memory, the Physical AI  gets a body, for example, a robot, self-driving car, or smart machine. Imagine a robot that does not only figure out how to pick up a cup but actually reaches for it, grabs it, and hands it to you. Physical AI connects thinking (algorithms) to real-world action. To do this, it needs: Eyes and ears through sensors (cameras, microphones, radar) to see and hear. A brain which are the processors to understand what is happening. Arms and legs through motors, wheels, or grippers so that it can move and interact. SenseRobot: AI-Powered Smart Chess Coach and Companion (Source) Just take an example of a self-driving car, which does not only think about driving but uses cameras to spot stop signs, calculates when to brake, and actually physically presses the brake pedal. Similarly, a warehouse robot that may use AI to find a package, navigate around people, and lift it with mechanical arms. MARS rover uses AI to identify organic materials in the search for life on Mars (Source) Why does this matter? Because traditional AI is like a smart assistant on your phone, it can talk or answer queries, but it can not do anything physical. On the other hand, Physical AI can act. It can build things, clean your house, assist surgeons, or even explore Mars. By giving AI a body, we’re turning it from a tool that thinks into a partner that acts. This will change the way we live, work, and solve problems in the real world. So, we can say that a traditional AI is the brain that thinks, talks, and calculates. Whereas, Physical AI is the brain and the body that thinks, sees, moves, interacts and is possible indeed. Physical AI vs. Embodied AI Although Physical AI and Embodied AI seem similar at a glance. They are quite different. Let's understand the difference between the two. The Physical AI systems are integrated with physical hardware (sensors, actuators, robots etc.) to interact with the real world. The main focus in Physical AI  is to execute tasks in physical environments. It combines AI algorithms with mechanical systems and can perform operations such as movement, grasping, navigation. This type of AI relies on hardware (motors, cameras, wheels) to interact with surroundings. An example of Physical AI are self-driving cars that use AI to process sensor data (cameras, radar) and physically control steering, brake, or acceleration. Another example is warehouse robots like Amazon’s Sparrow that use AI to identify, grab, and sort packages.  Embodied AI systems on the other hand are designed to learn and reason through physical interaction with their environment. They focus on intelligence that comes from having a body. The emphasis in Embodied AI is on intelligence that comes from a body’s experiences similar to humans who learn by touching, moving, and interacting. The goal of Embodied AI is to learn skills (e.g., walking, grasping) through trial and error in the real world. Framework of Embodied Agent (Source) An example of Embodied AI is Atlas Robot from Boston Dynamics that learns to balance, jump, or navigate uneven terrain by adapting its body movements.   We can summarize the difference between the Physical AI is the AI with a body that acts to solve practical problems (e.g., factory automation) and Embodied AI is the AI that needs a body to learn to improve intelligence (e.g., teaching robots common sense through interaction). The Promise of Physical AI The promise of Physical AI lies in its ability to bring digital intelligence into the tangible physical world. Physical AI is revolutionizing the way machines work alongside humans and transform different industries. Following are key sectors where Physical AI is set to make a huge impact. Healthcare There are many applications of Physical AI in healthcare. For example, surgical robots use AI-guided systems to perform minimally invasive surgeries with precision. Wearable robots such as rehabilitation exoskeletons help patients regain mobility by adapting to their movements in real time. AI powered autonomous robots deliver supplies, sanitize rooms, or assist nurses with repetitive tasks. Exoskeleton control neural network (Source) Manufacturing In manufacturing, collaborative robots (Cobots) are the AI-powered arms that work alongside humans. Cobots learn to handle delicate tasks like assembling electronics or doing more complex tasks that require precision similar to human hands.  Techman AI Cobot (Source) Agriculture In agriculture, AI-driven machines plant, water, and harvest crops while analyzing soil health. Weeding robots use computer vision to identify and remove weeds without chemicals and autonomous tractors drive themselves, avoid obstacles using computer vision and other sensor data and perform various farm tasks, from mowing to spraying. These autonomous tractors use sensors, GPS, and artificial intelligence (AI) to operate without a human in the cab. Driverless tractors perform fully autonomous spraying tasks at a Texas vineyard (Source) Logistics & Retail In Logistics & Retail, Physical AI power robots that sort, pack, and deliver goods with speed and accuracy. These robots use real-time decision-making with adaptive learning to handle a variety of products. For example, Proteus robots sort, pack, and move goods autonomously. Other machines like drones or delivery robots (e.g., Starship) navigate  to deliver packages. Amazon Proteus Robot (Source) Construction Physical AI has an important role to play in transforming how humans do construction. AI-driven excavators, bulldozers, and cranes operate autonomously or semi-autonomously to perform tasks like digging, leveling, and material placement. Companies like Caterpillar and Komatsu are leveraging AI to create smarter heavy machinery. AI-powered robotic arms can perform repetitive tasks like bricklaying, welding, and concrete finishing with high precision. Komatsu Autonomous Haulage System (AHS) (Source) Physical AI is redefining industries by turning intelligent algorithms into real-world action. From hospitals to highways, its ability to act in the physical world will create robots and machines that are not just tools, but partners in solving humanity’s greatest challenges. Data and Hardware Challenges in Physical AI The data and hardware challenges in Physical AI revolve around deploying and executing AI models within hardware systems, such as industrial robots, smart devices, or autonomous machinery. This creates some unique challenges related to data and hardware as discussed below. Data Challenges Availability of High Quality Data As with the many other AI systems, this is also an issue with Physical AI. Physical AI systems often require large, precise datasets to train models for tasks like defect detection and path planning etc. These datasets must reflect the exact physical conditions (e.g., lighting, material properties) of the deployment environment. For example, a welding robot needs thousands of labeled images of welds of different metals under various factory conditions and images taken from different angles to train a vision system. Such data is often not available and collecting it manually is costly and time-consuming. Data Annotation and Labeling Complexity Physical AI systems require accurately annotated data on a variety of data samples for training which require domain expertise and manual labeling effort. Since the AI must act in real physical condition it must be trained on all possible types of conditions the system may face. For example, training a Physical AI system to detect weld imperfections requires engineers to annotate thousands of sensor readings or images in which labeling error by humans may be possible. Adapting to New Situations Physical AI systems are trained on fixed datasets that don’t evolve post-deployment. It may be possible that physical settings (such as change in the environment, place or equipment) in which Physical AI is deployed may change which makes it hard for pre-trained models to work. For example, a robotic arm trained to assemble a specific car model might struggle if the factory switches to a new design. In such cases the model becomes obsolete and requires retraining with fresh data. Hardware Challenges Computational Power and Energy Constraints Running AI models such as deep learning for computer vision on physical hardware requires significant computational resources. Such types of AI models often exceed the capabilities of embedded systems. Battery-powered devices (e.g., IoT sensors) or small robots may also face energy limits and industrial systems need robust cooling. For example, a FANUC welding robot may use a GPU to process sensor data, but integrating this into a compact, energy-efficient unit is costly and generates heat. This may result in hardware failure in a hot environment in the factory. Sensor Limitations and Reliability Physical AI depends on sensors (e.g., cameras, LIDAR, force sensors) to perceive the environment. Sometimes these sensors may not give precise reading or fail under harsh conditions (e.g., dust, vibration). Calibrating these sensors repeatedly can also degrade its performance. For example, a camera on a robotic arm may misjudge weld alignment in poor lighting or if dust obscures the lens which leads to defective outputs. Integration with Legacy Hardware Many physical systems such as factory robots or HVAC units need modern AI models running on outdated processors or proprietary interfaces. Deploying such AI models into these systems is technically challenging and expensive. For example, upgrading a 1990s-era manufacturing robot to use AI for defect detection may require replacing its control unit which may disrupt the production lines. Latency and Real-Time Processing Needs Physical tasks such as robotic welding or autonomous navigation require real-time decision making that must happen in precise milliseconds but AI inference on resource-constrained hardware introduces latency issues. If the AI model is migrated to the cloud, the delays may occur due to network issues. For example, a welding robot adjusting its path in the middle of the welding process might lag if its AI model runs on a slow CPU which results in uneven welds. AI Alignment Considerations The AI alignment problem refers to the challenge of ensuring that AI systems act in ways that are aligned with human values, goals, and ethical principles. This problem becomes especially critical as AI systems become more capable and autonomous. The misaligned AI could potentially cause harm, either unintentionally or due to conflict in objectives. In the context of Physical AI the alignment problem takes on additional layers of complexity as AI systems interact with the physical world. Following are the key alignment problems related to physical AI. Real-World Impact Physical AI systems have direct impact in the physical world. Misalignment in these systems can lead to physical harm, property damage, or environmental disruption. For example, a misaligned autonomous vehicle might prioritize efficiency over safety but it may sometimes lead to accidents. Therefore, ensuring that physical AI systems understand and respect human intentions in real-world environments is a significant challenge. Unpredictable Environments Physical AI operates in environments that are often unpredictable and complex. This makes it harder to train such AI models in all possible scenarios. This increases the risk of unintended behavior. For example, a household robot may misinterpret a human’s command in a way that leads to dangerous actions, such as mishandling objects or entering restricted areas. Ethical and Social Considerations Physical AI systems often operate in shared spaces with humans which can raise ethical questions about privacy, consent, and fairness. Misalignment could lead to violations of these principles. For example, a surveillance robot may overstep boundaries in monitoring public spaces which can lead to privacy concerns especially in areas like international boundaries between two countries. The AI alignment problem in Physical AI is not just about getting the AI algorithms right but it's also about integrating intelligence into machines that interact safely and beneficially with the physical world. Encord's Role in Advancing Physical AI Encord plays an important role in advancing Physical AI by enabling developers with the tools needed to efficiently manage and annotate multimodal data for training models. Accurately annotated data is essential for training intelligent systems that interact with the physical world. In Physical AI, robots and autonomous systems rely on a variety of data streams in the form of high-resolution images and videos to sensor readings like LiDAR and infrared to understand their environments and make decisions. Encord platform enables the process of annotating and curating this heterogeneous data and ensures that the AI models are trained on rich, accurate datasets that capture the complexities of real-world environments. For example, consider the customer story of Four Growers. Four Growers is a robotics and AI company that creates autonomous harvesting and analytics robots for agriculture,  starting in commercial greenhouses. Four Growers uses multimodal annotation capabilities of Encord to label vast amounts of agricultural imagery and sensor data collected via drones and field sensors. This annotated data is then used to train models that power robots capable of precise crop monitoring and yield prediction. The integration of such diverse data types ensures that these AI systems can adapt to varying lighting conditions, detect changes in crop health, and navigate complex field terrains which are all critical for automating agricultural processes and optimizing resource management. Tomato Harvesting Robot by Four Growers (Source) The robot uses high-resolution images and advanced sensors to capture the detailed spatial data across the field. This information is used to create yield heatmaps that offer a granular view of crop performance. These maps show fruit count and yield variations across different parts of the field.   When the robot is harvesting, its AI model helps in identifying and localizing tomatoes among the plant but also analysing its ripeness. By detecting the current ripeness and growth patterns, the system predicts how many tomatoes will be ripe in the coming weeks. Encord helps in the annotation and processing of multimodal data  to train this kind of Physical AI system. Tomato Yield Forecasting (Source) Encord helps to accelerate the development of robust AI models for Physical AI by providing tools to prepare high-quality, multimodal training datasets. Whether it’s in agriculture, manufacturing, healthcare, or urban management, Encord platform is a key enabler in the journey toward smarter, safer, and more efficient Physical AI systems. Key Takeaways Physical AI is transforming how machines interact with our world by integrating AI into physical systems like robots, drones, and autonomous vehicles. Following are the key takeaways from this blog: Physical AI uses AI with sensors, processing units, and mechanical hardware to enable machines to understand, learn, and perform tasks in real-world environments. Physical AI focuses on executing specific tasks in the real-world, whereas Embodied AI emphasizes learning and cognitive development through physical interactions imitating human experiential learning. Physical AI is set to revolutionize industries by automating complex tasks, improving safety and efficiency, and unlocking multi-trillion-dollar markets. Successful deployment of Physical AI depends on overcoming data quality, hardware constraints, sensor reliability, and ethical AI alignment challenges. Encord offers powerful tools for annotating and managing multimodal data to train Physical AI.

Intralogistics is the backbone of modern supply chains. It ensures a smooth movement of goods within warehouses, distribution centers, and manufacturing facilities. As businesses scale, optimizing internal logistics becomes critical for efficiency, cost reduction, and meeting consumer demands. With the rise of automation, robotics, and AI-driven logistics, companies are increasingly investing in intralogistics solutions to enhance productivity. But what exactly is intralogistics, and why should organizations care? What is Intralogistics? It is the flow of materials, goods, and data within a facility like warehouse, factory, or a fulfilment center. This also includes processes like storage, inventory management, material handling, and order fulfilment. Traditional logistics focus on external transport systems whereas interlogistics optimizes internal workflows using automation, robotics, and other AI powered systems. Businesses prioritize intralogistics to reduce operational costs, minimize errors, and improve supply chain agility. Components of Intralogistics Intralogistics have three core elements: Material Flow: The movement of goods within a facility, including receiving, storage, picking, packing, and shipping. Data Management: Using real-time data and analytics to provide visibility into inventory levels, order statuses, and equipment performance. Warehouse Management: Coordinating warehouse operations from inventory control to space optimization and labor allocation.  Why Intralogistics Matters? Efficiency Gains: Streamlining operations improves order accuracy and reduces delays. Cost Reduction: Optimized workflows lower labor costs and minimize waste. Scalability: AI-driven intralogistics adapts to business growth and fluctuating demand. Sustainability: Efficient flow of goods reduces energy consumption and carbon footprint. Use Cases of Internal Logistics Warehouse Automation Warehouses use robots and conveyor belts to transport products faster with fewer mistakes. Autonomous Mobile Robots (AMRs) and Automated Guided Vehicles (AGVs) transport goods, while robotic arms help with picking and packing. The conveyor belts and sortation systems ensure a smooth flow of inventory. AI warehouse management systems (WMS) track inventory in real-time, preventing stockouts and optimizing storage space. Source Manufacturing and Production Lines Factories use conveyor systems to move materials quickly between workstations. Conveyor systems move raw materials through different stages of production with minimal human intervention. Just-in-time (JIT) inventory systems are used to ensure the required parts arrive exactly when needed to avoid delays and also to reduce storage cost. Businesses also use AI models to forecast demands to help manufacturers keep an eye on the stock levels and avoid overstocking. E-commerce Fulfillment Centers Online retailers use automated storage and retrieval systems to speed up picking and packing. Automated storage and retrieval systems (AS/RS) organize inventory for fast picking and packing. AI-powered sortation systems classify and route packages efficiently, reducing delivery times. This helps businesses process more orders more efficiently with fewer errors. Cold Chain Logistics for Pharmaceuticals Temperature-sensitive goods, like vaccines and perishable medicines, require precise handling. Internal logistics processes, such as IoT-enabled storage systems, monitor temperature and humidity levels in real-time to ensure compliance with regulatory standards. Automated material handling reduces human error and ensures fast, safe transportation of critical healthcare supplies. Source Retail and Grocery Distribution Retailers use automated warehouses to restock shelves quickly. AI helps predict demand, so stores don’t overstock or run out of items.  Challenges in Scaling Intralogistics Scaling logistical flow internally comes with several challenges, from handling massive amounts of real-time data to integrating automation into legacy systems Data  Data is at the core of intralogistics. Warehouses, fulfillment centers, and manufacturing plants rely on a huge network of sensors, automation tools, and analytics to optimize product flow. However, managing and processing this data at scale presents several issues: Real Time Tracking and Visibility Accurate tracking of inventory, equipment, and shipments is critical for efficient intralogistics. But ensuring real-time visibility is difficult due to: Signal Interference: RFID and GPS-based tracking systems often face disruptions in large warehouses, affecting location accuracy. Data Latency: Delays in updating inventory counts or shipment status can lead to errors in order fulfillment. Scalability Issues: As operations expand, managing a growing network of connected sensors and devices becomes complex. Data-centric AI can clean and standardize tracking data, improving accuracy by filtering out inconsistencies and detecting anomalies in real time. Integrating Diverse Data Sources Intralogistics systems heavily depend on various sensors like RFID scanners, weight sensors, LiDAR, and camera. Each system also interact and rely on data from other systems as well. Hence, integrating and analysing data from these diverse sources presents challenges: Inconsistent Data Formats: Different vendors use different data structures, making it difficult to merge information. Conflicting Readings: One sensor may detect an object, while another fails to register it, leading to errors in automation. Processing Bottlenecks: High volumes of sensor data require powerful computing resources to ensure operational efficiency. Sensor fusion techniques can align, filter, and cross-validate information, ensuring accurate and consistent data for robotic systems and warehouse automation. Data Analytics and Decision Making Handling a large amount of data generated also lead to many challenges: Extracting Insights from Raw Data: AI models require well-structured, high-quality datasets for effective decision-making. Managing Unstructured Data: Video feeds, IoT logs, and sensor data need to be converted into actionable insights. Security and Compliance Risks: Protecting sensitive logistics data from cyber threats while ensuring regulatory compliance adds complexity. Infrastructure  Many companies operate with legacy warehouse management software (WMS) and enterprise resource planning (ERP) software which are not designed for automation. Integrating new technology with existing infrastructure presents challenges such as: Compatibility Problems: Older systems may lack APIs or support for AI tools and robotic automation. Scalability Constraints: Expanding automation across multiple facilities requires a standardized approach, which is difficult when working with different vendors. Network Reliability: High-speed, stable connectivity is crucial for seamless machine-to-machine communication, yet many warehouses lack the necessary infrastructure. Specially designed adaptable softwares can be used as an intermediary layer, bridging data gaps between legacy systems and modern automation tools through intelligent API integrations and real-time processing. Cost and ROI Concerns for Automation While automation enhances efficiency, the high upfront investment in robotics, AI, and IoT devices raises concerns about the return of investment. The businesses need to consider the following: Implementation Costs:  AI logistics solutions require significant initial investment in hardware, software, and training. Long Payback Periods: Efficiency gains take time to materialize, making it difficult to justify costs in the short term. Ongoing Maintenance Expenses: Automated systems require continuous updates and repairs, adding to operational costs. Still the businesses can leverage AI to optimize automation deployment by identifying high-impact areas for investment. This way businesses can achieve cost savings and efficiency improvements faster. Workforce Adaptation and Training As intralogistics systems become more automated, the role of human workers shifts from manual tasks to overseeing and maintaining the automation tools. However, you can face challenges in: Upskilling the Workforce: Traditional warehouse workers may lack experience in AI, robotics, and automation, requiring extensive training or hiring the right talent. Human-Machine Collaboration: Many intralogistics systems require workers to work alongside AI-driven robots, requiring new skills and training. How Encord Helps Build Intralogistics Tools Without accurate, well-labeled data, warehouse robots struggle to detect objects, navigate spaces, or pick and pack items correctly. That’s where Encord comes in. Encord provides a platform to build data centric AI solutions for intralogistics systems.  Source AI systems for intralogistics are trained on diverse sensor data for warehouse automation, robotic navigation, and quality control. However, training reliable AI models requires accurate, well-labeled datasets. Encord’s data platform enables: Automated Video & Sensor Data Labeling: Encord supports video, LiDAR, and multi-sensor data annotation, making it easy to build a robust training dataset to build AI models for warehouse robotics. Active Learning for Faster Model Improvement: AI-assisted annotation speeds up dataset creation while improving model accuracy. Collaborative Workflow Tools: Teams can manage, review, and scale data labeling efficiently. Ensure Continuous Model Optimization: Encord’s platform allows teams to refine datasets over time, improving AI warehouse automation. Real-World Applications Here are some of the case studies of large enterprises that have successfully implemented internal supply chain solutions. Robots in Amazon Fulfilment Centers Amazon is a prime example of how intralogistics processes can scale operations for massive global demand. It uses AMRs and AMVs in its fulfilment centers for transportation of goods within its warehouses. With over 175 fulfillment centers worldwide, Amazon’s use of intralogistics technology has allowed the company to manage a highly complex network while maintaining quick delivery times, even during peak seasons. The efficiency of the automated system has significantly cut down operational costs and improved order accuracy. Toyota’s Manufacturing Platform Along with AGVs in its manufacturing plants to improve warehousing, Toyota also built an AI driven platform which integrates data from various stages of production to improve decision making. By using ML algorithms the platform predicts potential bottlenecks and maintenance issues. This predictive approach reduces downtime and enhances the overall efficiency of production. Toyota also adopted hybrid cloud solutions to connect its manufacturing facilities globally. This cloud infrastructure allows Toyota to gather real-time data from machines, sensors, and robots across its factories, providing a unified view of its operations.  Source The integration of AI into its supply chain allows Toyota to predict maintenance needs, optimize the movement of parts with AGVs, and improve production flexibility. Walmart Improving Distribution with Automation Walmart, the world’s largest retailer, has long been a leader in logistics innovation. To keep up with its massive scale, Walmart has adopted several intralogistics technologies to optimize its distribution centers and stores. Automated Sortation and Conveyor Systems Walmart uses AI sortation systems to process and distribute goods within its distribution centers. The system directs items to the appropriate shipping lanes, speeding up the sorting process. Robotic Palletizing Walmart has also experimented with robots, where robotic forklifts are used to stack products onto pallets. This reduces manual labor while maintaining precision, making it easier for Walmart to manage its inventory and prepare orders for shipping. Conclusion These real-world examples demonstrate the power of intralogistics in transforming supply chains across various industries. From Amazon’s robotic fulfillment centers to Toyota’s automated manufacturing lines, the adoption of AI, robotics, and automation has allowed businesses to streamline operations, improve accuracy, reduce costs, and scale rapidly. As more companies adopt intralogistics, the future of supply chain management will increasingly depend on technological advancements to drive efficiency and meet the growing customer demands. 📘 Download our newest e-book, The rise of intelligent machines to learn more about implementing physical AI models.

The global smart robot market is experiencing rapid growth, with projections estimating it will reach approximately $834 billion by 2037. This growth is driven by advancements in artificial intelligence (AI), deep learning, and sensor technologies that enable autonomous robots to perform complex tasks across various industries. Traditional robots operate based on pre-programmed instructions and perform specific tasks. However, smart robots can perceive their environment, learn from their experiences, and autonomously adapt to new situations. Moreover, smart robots contribute to substantial cost savings. For instance, the U.S. Air Force has implemented robotic solutions that have saved approximately $8.8 million since 2016, equating to $220,000 per aircraft in maintenance costs. Despite their transformative potential, developing smart robots poses significant challenges, from managing massive datasets and fine-tuning advanced algorithms to addressing the complexities of real-world environments. In this post, we will discuss what smart robotics are, their use cases, benefits, and challenges. We will also go over how platforms like Encord can help overcome data issues and help experts build more efficient autonomous robotic systems.  What is Smart Robotics? Smart robots are autonomous machines designed to perform complex physical tasks using advanced robotics technologies, AI, and ML. They adapt to changing environments and work alongside humans to assist them in several domains. For example, Amazon uses mobile robots called Proteus, which work collaboratively with human staff. These robots can coordinate directional changes and assist humans with navigation using advanced vision. The technique improves operational efficiency while maintaining safety and streamlining workflows in dynamic environments. Proteus, Amazon’s autonomous mobile robot Core Components of Smart Robotics Smart robots use several components to process information and act appropriately. Below, we will discuss the key components of smart robotics. Sensors and Perception Smart robots interpret their surroundings using different sensors. Visual sensors, such as cameras and LiDAR systems, provide detailed spatial data, while auditory and tactile sensors help them understand the environment in different dimensions. Sensors collect important data such as distance, texture, temperature, and movement from different sources. Fusing this data allows the robot to create a comprehensive model of its environment, enabling accurate navigation and informed decision-making in real time. Processing Units and Artificial Intelligence Processing units in smart robots act as a "brain," often including Central Processing Units (CPUs), Graphics Processing Units (GPUs), and specialized AI accelerators. These units are integrated with advanced AI algorithms to handle the massive influx of sensory data in real time. Processing units run ML algorithms, particularly neural networks, to enhance robot intelligence.  For instance, robots on the factory floor use AI to plan efficient routes and refine their paths by learning from past trips. This cognitive capability distinguishes smart robots from traditional machines with fixed programming. Actuators and Movement Mechanisms After the robot perceives its environment and processes the necessary data, actuators help convert the information into physical action. These actuators act like motors or hydraulic systems to execute movements and interactions. The robot's ability to perform tasks depends on the seamless coordination between perception and action. The processing unit, guided by sensor data and AI, directs the actuators to execute specific movements, enabling the robot to navigate, manipulate objects, and carry out its intended tasks within its environment. The Six Most Common Types of Smart Robots Robots come in various forms, each designed for specific tasks and environments. Here are six common types of robots: Autonomous Mobile Robots (AMRs) AMRs operate independently and can navigate their environment intelligently without needing physical guides or pre-programmed paths. They use sensors and onboard processing to perceive their surroundings, map environments, and make decisions about navigation and task execution.  AMRs are flexible, adaptable, and ideal for dynamic environments like warehouses, hospitals, and public spaces. Autonomous mobile robot Automated Guided Vehicles (AGVs) AGVs are material-handling robots that follow predefined paths using wires, magnetic strips, or lasers. Unlike AMRs, AGVs are less flexible as they follow fixed routes and need changes to the setup, like moving strips or wires, to adjust their paths. However, they are suitable for repetitive tasks like moving parts along a factory assembly line or carrying boxes to a shipping area.  Automated guided vehicles Articulated Robots Articulated robots are robotic arms with rotary joints (similar to those of a human arm) that allow for a wide range of motion and flexibility. They usually have two to ten joints or more.  Articulated robots are used for various applications, such as assembly, welding, painting, and material handling in manufacturing and industrial settings. Their dexterity and reach make them suitable for complex and precise tasks, like assembling tiny phone parts or welding car frames. Articulated robots - robotic arms  Humanoids Robots Mobile humanoid robots can mimic human form and behavior for tasks that require human-like interactions. They are developed for research, education, and public relations, focusing on exploring human-robot interaction. For instance, Pepper from SoftBank Robotics welcomes guests and promotes products at events, serving as a friendly face for public relations. Although still under development for broad practical use, organizations are considering them for use in customer service, elder care, and potentially dangerous environments.  For example, Stanford’s OceanOneK, a humanoid diving robot, explores deep-sea shipwrecks at depths reaching 1,000 meters, where conditions are too hazardous for human divers. Humanoid robots Collaborative Robots (Cobots) Cobots work safely alongside humans in a shared workspace. They are equipped with sensors and safety features to detect human presence and avoid causing injury.  Compared to traditional industrial robots, collaborative robots are smaller, can be used more flexibly, and are easier to program. They assist humans across various tasks, boosting productivity and safety in manufacturing, assembly, and certain service applications. Collaborative robots Hybrid Robots Hybrid robots combine various capabilities of different robot types, such as wheeled mobile robots, aerial drones, or robotic arms. Their  flexibility allows them to handle tough jobs that need multiple skills like flying high to check crops or gripping tools to fix underwater pipes. These autonomous systems are ideal for complex workflows that require versatility and precision. Hybrid robot Why Smart Robots Are Gaining Popularity  Smart robots are experiencing increased adoption across various industries due to their potential to enhance productivity, efficiency, and safety. Several factors contribute to their growing popularity: Improved Productivity: Smart robots automate repetitive tasks, freeing human workers for more complex responsibilities. They boost productivity for large manufacturers by enabling continuous operations without extra labor costs. Enhanced Efficiency: Smart robots streamline warehouse operations by automating inventory management and order fulfillment, significantly reducing operational costs. For instance, Amazon warehouses featuring robots like Proteus have achieved up to a 25% reduction in operational costs and savings of up to $10B/year. Increased Safety: Smart robots can handle hazardous tasks, reducing the risk of accidents and injuries. In industries like construction, robots assist in tasks such as bricklaying, welding, and demolition, increasing efficiency and safety on-site. Predictive Maintenance: Smart robots use advanced sensors and ML algorithms to detect and analyze data from equipment, identifying potential issues before breakdowns occur. This enables the scheduling of maintenance activities in advance, reducing downtime and extending machinery life. Enhanced Product Quality: Smart robots can detect flaws during manufacturing with integrated sensors and data analysis capabilities This reduces the number of defective products reaching the market. They can also monitor production processes in real-time, adjusting settings to improve quality. Reduced Overhead Costs: Smart robots can deliver quick returns on investment by automating specific job roles and lowering health and safety costs. They also require less space and can work alongside humans, allowing businesses to downsize to more cost-effective workplaces. Consumer and Commercial Applications of Smart Robotics Households and workplaces are quickly adopting smart robots to simplify tasks and enhance productivity. Below are key areas where their versatility makes them valuable in both consumer and commercial settings.   Consumer Applications Smart robots are becoming more integrated into our homes, improving convenience, companionship, and assistance in daily life. Smart Home Assistants Robotic vacuums like the Roomba iRobot use AI and sensors to autonomously navigate homes, clean floors, and adapt to changing layouts. These robots learn user habits over time and optimize cleaning schedules and routes for maximum efficiency. iRobot Roomba Companion Robots Beyond chores, robots like Pepper or ElliQ interact with humans, provide companionship, and assist the elderly. They can monitor daily routines, remind users to take medications, and provide entertainment, enhancing the quality of life for vulnerable populations. ElliQ companion robot Commercial Applications In the commercial sector, smart robots streamline operations, reduce costs, and enable businesses to scale efficiently. Manufacturing Collaborative robots (cobots) such as ABB’s YuMi or UR5e solder work alongside humans on production lines. In electronic manufacturing, cobots solder tiny components with unmatched accuracy, cutting errors and speeding up output. They handle repetitive or hazardous tasks, letting workers focus on higher-value roles. ABB’s YuMi robot Warehouse Automation Autonomous mobile robots (AMRs) from companies like Fetch Robotics (acquired by Zebra Technologies) and Locus Robotics maintain high throughput in large-scale e-commerce and logistics operations. These robots zip around warehouses, retrieving items, delivering them to pickers, and restocking shelves, all without human guidance.  Locus Robotics fulfillment archives Healthcare Surgical robots like da Vinci bring AI-enhanced precision to operating rooms. Surgeons use robotic arms to perform minimally invasive procedures, like heart surgeries, with smaller incisions, leading to faster recoveries. Meanwhile, disinfection robots welding UV light sanitizer hospital spaces, reducing infection risks without harming staff. Da Vinci surgical robot Learn how to use Encord Active to enhance data quality using end-to-end data preprocessing techniques. Security AI-powered surveillance robots provide proactive and responsive solutions in the security and surveillance domain. Security robots like SAM3 can monitor environments continuously without constant human intervention, which is valuable in critical security environments. They can also react instantly to suspicious events, alerting human operators. Autonomous security robot SAM3 Best Practices for Building Smart Robotics Developing and implementing smart robotic solutions requires careful planning and execution. These best practices can help you maximize the benefits of smart robotics while minimizing potential challenges. Define Clear Objectives: Before you start building a smart robot, be clear about what it needs to do. What problems are you trying to solve? What specific tasks will the robot perform? Clearly defining the goals for implementation is the first and most important step.   Choose the Right Technology: Select appropriate sensors, processors, actuators, and AI algorithms based on the application's specific requirements. When choosing hardware and software components, consider factors such as accuracy, reliability, and compatibility. Focus on Integration and Interoperability: Ensure seamless integration between different components of the robotic system and with existing IT infrastructure. Try to use open standards and protocols to promote interoperability and avoid vendor lock-in. Prioritize Safety and Security: Implement powerful safety measures to protect humans working alongside robots, including safety barriers, photoelectric barriers, and scanners in monitored zones. Incorporating security measures can help you to protect your robot from data theft and unauthorized access. Focus on Learning and Adaptation: Smart robots get smarter over time by learning. Machine learning techniques enable robots to learn from experience and adapt to changing environments. Data fusion combines data from different sensors to form a comprehensive understanding of the surroundings. Promote Human-Robot Collaboration: Robots work as helpers, so design them in a way that they can work alongside humans, augmenting their capabilities and improving productivity. Provide training and support to human workers to ensure effective collaboration with robots. Use Simulation and Testing: Before deploying your robot physically, employ simulation tools to test and refine its capabilities in a virtual environment. Use iterative testing cycles to allow for quick adjustments and improvements. Monitor Performance and Optimize: Continuously monitor smart robot performance and identify areas for improvement. Use data analytics to optimize robot behavior and enhance overall system efficiency. Learn how to boost data quality in our Complete Guide to Gathering High-Quality Data for AI Training What are the Challenges with Smart Robots Today? Despite the advancements and potential benefits of smart robots, several challenges make their broad adoption and optimal performance difficult. Data challenges stand out as one of the most critical barriers to achieving the full potential of smart robotics. Data Quality and Quantity: Smart robots require large amounts of high-quality data to learn effectively. Insufficient or inaccurate data can impede their learning and performance. Acquiring enough representative data to reflect real-world situations can be both difficult and expensive. Data Annotation and Labeling Complexity: ML models within intelligent robots rely on accurately labeled data. The annotation process is labor-intensive, time-consuming, and prone to human error, which can slow down the development and refinement of robotic capabilities. Real-Time Data Processing: Smart robots must understand the world as it happens, not later. They constantly get data from sensors and process it quickly to make decisions in real time. Processing all this sensor data requires powerful computers and scalable software that can handle large data volumes. Data Security and Privacy Concerns: Smart robots collect large amounts of data about their environments, some of which may be sensitive. Ensuring the security and privacy of this data requires robust measures and clear protocols, adding complexity and cost to robot development. High Development and Operational Costs: The initial investment in smart robotics, including research and development, hardware, and system integration, can be substantial. Ongoing expenses related to maintenance, upgrades, and continuous AI model training further affect affordability. How Encord Helps Build Smart Robotics As discussed above, building efficient smart robots presents numerous challenges, primarily due to the inherent data complexities. Smart robotics relies heavily on high-quality data to train AI models, and issues like noisy sensor inputs, inconsistent annotations, and real-time processing can negatively impact performance. Advanced data management tools like Encord are necessary to address these data challenges. Encord is a leading data development platform for AI teams that offers solutions to tackle issues in robotics development. It enables developers to create smarter, more capable robot vision models by streamlining data annotation, curation, and visualization. Below are some of its key features that you can use for smart robotics development. Intelligent Data Curation for Enhanced Data Quality Encord Index uses semi-supervised learning to assess data quality and detect anomalies, such as blurry images from robotic cameras or misaligned sensor readings. It can detect mislabeled objects or actions and rank labels by error probability. The approach reduces manual review time significantly. Precision Annotation with AI-Assisted Labeling for Complex Robotic Scenarios Human annotators often struggle to label the complex data required for smart robots. Encord addresses this through advanced annotation tools and AI-assisted features. It combines human precision with AI-assisted labeling to detect and classify objects 10 times faster. Custom Ontologies: Encord allows robotics teams to define custom ontologies to standardize labels specific to their robotic application. For example, defining specific classes for different types of obstacles and robotic arm poses. Built-in SAM 2 and GPT-4o Integration: Encord integrates state-of-the-art AI models to supercharge annotation workflows like SAM (Segment Anything Model) for fast auto-segmentation of objects and GPT-4o for generating descriptive metadata. These integrations enable rapid annotation of fields, objects, or complex scenarios with minimal manual effort. Multimodal Annotation Capabilities: Encord supports audio annotations for voice model used robots that interact with humans through voice. Encord’s audio annotation tools use foundational models like OpenAI’s Whisper and Google’s AudioLM to label speech commands, environmental sounds, and other auditory inputs. This is important for customer service robots and assistive devices requiring precise voice recognition. Maintaining Security and Compliance for Robotics Data Encord ensures data security and compliance with SOC2, HIPAA, and GDPR standards, which are essential for managing sensitive data in robotics applications. Security is critical when handling potentially sensitive information like patient medical images used in surgical robots or personal voice data collected by companion robots. Encord’s commitment to security ensures data protection throughout the AI development lifecycle.   Smart Robots: Key Takeaways Smart robotics is transforming industries by improving productivity, efficiency, and safety. These AI-powered machines autonomously execute tasks, learn from their surroundings, and work alongside humans. Below are some key points to remember when building and using smart robotics. Best Use Cases for Smart Robotics: Smart robotics excels in dynamic and complex environments that require automation, adaptability, and efficiency. This includes streamlining manufacturing assembly lines, optimizing warehouse logistics and fulfillment, enhancing surgical precision in healthcare, providing proactive security and surveillance, and delivering intelligent assistance in smart homes and elder care. Challenges in Smart Robotics: AI requires a large amount of high-quality data for effective learning, but collecting and labeling this data is complex and time-consuming. Real-time data processing is essential for robots to respond quickly and accurately, yet achieving this remains a hurdle. Also, ensuring data security and privacy is critical to prevent risks. Overcoming these challenges is essential for building reliable, high-performing smart robotic systems. Encord for Smart Robotics: Encord’s specialized data development platform, featuring AI-assisted annotation tools and robust data curation features, enhances the quality of training data for smart robots. These tools streamline data pipelines, improve data quality and quantity, ensure cost-effectiveness, and maintain data security. They can help the development and deployment of smarter, more capable robotic systems. 📘 Download our newest e-book, The rise of intelligent machines to learn more about implementing physical AI models.

Did you know the global e-commerce market is expected to reach $55.6 trillion in 2027? Research from the Harvard Business Review shows that emotional factors drive 95% of purchasing decisions, highlighting the importance of understanding customer sentiment for businesses. Yet, decoding these emotions at scale remains a challenge. A single Amazon product launch can generate thousands of reviews in days. Twitter sees 500 million daily tweets, many about brands. The volume is massive, but the real challenge is language. Human emotions are complex, and machines struggle to interpret them. This is where AI sentiment analysis becomes crucial. Using text analysis and natural language processing (NLP), businesses can decode customer sentiment and make sense of unstructured feedback data. The global sentiment analysis market is estimated to reach $11.4 billion by 2030. Businesses can automate the analysis of customer emotions, opinions, and attitudes at scale using artificial intelligence and machine learning models. However, building an effective tool comes with challenges, from ensuring high-quality datasets to overcoming linguistic complexities like negative sentiment, neutral sentiments, and contextual understanding. In this post, we’ll guide you step-by-step through the process of building your own AI sentiment analysis tool. Along the way, we will look at how platforms like Encord can help develop an AI sentiment analysis model that delivers actionable insights and improves customer experience.  Sentiment Analysis What is Sentiment Analysis? Sentiment analysis is an AI-driven technique that decodes emotions, opinions, and attitudes from unstructured data—text, audio, or video—to classify them as positive, negative, or neutral. It helps answer the question: How do people feel about a topic, product, or brand? Traditional methods depend on manual efforts, such as reading customer reviews, listening to customer support calls, or analyzing social media posts. However, with 80% of business data being unstructured, manual analysis is not scalable. AI can automate this process scale. For example, it can help with: Text Analysis: Scraping tweets like “This app changed my life!” or “Worst update ever, delete this!” to gauge brand sentiment. Audio Analysis: Detecting frustration in a customer’s tone during customer interactions over the phone. Multimodal Analysis: Combining facial expressions from video reviews with spoken words to better understand customer emotions. However, advanced models can classify emotions beyond just the polarity of positive or negative. They can also recognize emotions such as joy, anger, sadness, and even sarcasm. For example, a review stating, "The product was okay, but the delivery was terrible," would require the model to recognize mixed sentiment, neutral for the product and negative for the delivery. Challenges in AI Sentiment Analysis While AI-powered sentiment analysis has great potential for businesses, building a tool for it is not without its challenges, such as understanding the nuances of human language and the technical requirements of training AI models. Below, we discuss the key challenges of developing a sentiment analysis tool. Data Quality Issues Poor-quality or noisy data, such as misspelled words, irrelevant symbols, or inconsistent labeling, can degrade performance. Ensuring clean, well-structured datasets is critical but time-consuming. Contextual Understanding Human language contains nuances such as sarcasm, irony, and idiomatic expressions. A sentence like “Oh great, another delayed flight!” may seem positive at first glance, but it may be sarcastic. We need to use advanced natural language processing (NLP) methods and diverse datasets to help AI algorithms understand the context that reflects real-world situations.  Multilingual Support Sentiment analysis tools must support multiple languages and dialects for global businesses. However, linguistic differences, cultural contexts, and varying sentiment expressions (e.g., politeness in Japanese vs. directness in English) add layers of complexity. Automatically identifying textual data and applying sentiment analysis is essential, but building multilingual models demands extensive resources and expertise. Model Interpretability Many AI models, particularly those based on deep learning, function as "black boxes," which makes it difficult to understand how they reach particular conclusions. This lack of transparency can hinder trust and adoption for businesses. Ensuring model interpretability can overcome these issues. However, implementing interpretability is challenging because sometimes it requires simplifying complex models, which can reduce their accuracy or performance. Annotation Complexity Training accurate sentiment analysis models requires labeled data, but annotating large amounts of text or audio is labor-intensive and prone to human error. Ambiguities in language further complicate the process because different annotators may interpret the same text differently. Integration with State-of-the-Art Models The advancement of AI models such as GPT-4o and Gemini Pro and audio-focused models like Whisper brings both opportunities and challenges. Although these models provide state-of-the-art functionalities, integrating them into current workflows requires technical expertise and considerable computational resources. Tackling these challenges is crucial for building reliable sentiment analysis tools. Next, we’ll outline a process to create your AI sentiment analysis tool, using Encord to address data quality and annotation issues. How to Build an AI Sentiment Analysis Tool Building an AI sentiment analysis tool is a multi-stage process that transforms raw, unstructured data into actionable insights. From defining clear objectives to deploying models in real-world applications, each step requires careful planning, tools, and iterative refinement.  Below is a detailed guide to building your own sentiment analysis tool. It integrates machine learning, natural language processing (NLP), and platforms like Encord to streamline the annotation process. Step 1: Define Your Objective The foundation of any successful AI project lies in clarity of purpose. Begin by outlining the scope of your sentiment analysis tool. Will it analyze text (e.g., social media posts, customer reviews), audio (e.g., customer support calls, podcasts), or both?  For instance, a media company might prioritize multimodal analysis, combining video comments (text), tone of voice (audio), and facial expressions (visual). In contrast, a logistics company might focus solely on text-based sentiment from delivery feedback emails. Next, identify specific use cases. Are you aiming to improve brand monitoring by tracking social media sentiment during a product launch? Or optimizing customer support by detecting frustration in call center recordings? For example, a fintech startup could prioritize analyzing app store reviews to identify recurring complaints about payment failures.  Clear objectives guide data collection, model selection, and performance metrics, ensuring the tool aligns with business goals. Step 2: Collect and Prepare Data High-quality training data is the lifeblood of any AI model. Start by gathering raw data from relevant sources.  For text, this could include scraping tweets via the Twitter/X API, extracting product reviews from Amazon, or compiling customer emails from internal databases. Audio data might involve recording customer support calls or sourcing podcast episodes. However, raw data is rarely clean. Text often contains typos, irrelevant symbols, or spam (e.g., bot-generated comments like “Great product! Visit my website”).  Audio files may have background noise, overlapping speakers, or low recording quality. Preprocessing is critical: Text Cleaning: Remove HTML tags, correct misspellings (e.g., “gr8” → “great”), and filter out non-relevant content. Audio Cleaning: Isolate speech from background sounds using noise reduction tools like Adobe Audition or open-source libraries like LibROSA. Specialized tools like Encord can simplify this phase with automated preprocessing pipelines. For example, Encord's duplicate detection tool identifies redundant social media posts, while noise profiling flags low-quality audio files for review. A healthcare provider used Encord to clean 10,000+ patient feedback entries, removing 1,200 spam entries and improving dataset quality by 35%. Step 3: Annotate Data Using Encord Annotation, labeling data with sentiment categories like positive, negative, or neutral, is the most labor-intensive yet important phase. Manual labeling is slow and error-prone, especially for ambiguous phrases like “This app is fire… literally, it crashed my phone!” AI-powered annotation tools like Encord can streamline this process while addressing linguistic and technical challenges. Text Annotation Encord’s linguistic annotation framework enables granular labeling: Named Entity Recognition (NER): Identify brands, products, or people mentioned in the text. For example, tagging “iPhone 15” in the review “The iPhone 15 overheats constantly” helps link sentiment to specific products. Part-of-Speech (POS) Tagging: Parse grammatical structure to infer intent. Distinguishing “run” as a verb (“The app runs smoothly”) versus a noun (“Go for a run”) improves context understanding. Emotion Granularity: Move beyond polarity (positive/negative) to label emotions like sarcasm, urgency, or disappointment. Large Language Models (LLMs) like GPT-4o and Gemini Pro 1.5 are integrated into Encord’s workflow to pre-annotate text. For instance, GPT-4o detects sarcasm in “Love waiting 3 weeks for delivery! 🙄” by analyzing the eye-roll emoji and exaggerated praise. Human annotators then validate these suggestions, reducing manual effort by 60%. Customize document and text annotation workflows with Encord Agents. Audio Annotation Audio sentiment analysis introduces unique complexities: overlapping speakers, tonal shifts, and ambient noise. Encord’s layered annotation framework addresses these by enabling: Speech-to-Text Transcription: Automatically convert audio to text using OpenAI’s Whisper, which supports 100+ languages and accents. Tone & Pitch Analysis: Use Google’s AudioLM to tag segments as “calm,” “frustrated,” or “enthusiastic.” Sound Event Detection: Label non-speech elements (e.g., “door slamming,” “background music”) that influence context. Human-in-the-Loop Quality Control. Human-in-the-Loop Quality Control Encord’s active learning workflows prioritize ambiguous or impactful samples for review,  enabling annotators to focus on labeling data that affect model performance the most. For example, if a tweet is labeled as negative by some annotators and neutral by others, it gets flagged for review. This ensures accurate labeling, reduces bias and improves consistency, which are key factors for better AI models. Step 4: Train Your Model Once you have labeled your data, select a machine-learning framework or pre-trained model. For text, BERT and RoBERTa excel at understanding context, making them ideal for detecting sarcasm or nuanced emotions. Audio models like Wav2Vec 2.0 analyze tone and pitch, while hybrid architectures (e.g., Whisper + LSTM) combine speech-to-text with sentiment analysis. Fine-tuning adapts these models to your dataset: Pre-Trained Models: Start with a model trained on general data (e.g., BERT-base). Domain Adaptation: Train on your labeled data to recognize domain-specific terms, such as “CRP levels” in medical feedback or “latency” in gaming reviews. Class Imbalance: Address skewed datasets (e.g., 90% positive reviews) using techniques like oversampling minority classes or synthetic data generation with GPT-4o. Step 5: Evaluate Performance Testing on unseen data validates model reliability. Key metrics include: Precision: Measures how many predicted positives are correct (e.g., avoiding false alarms). Recall: Tracks how many actual positives are identified (e.g., missing fewer negative reviews). F1-Score: Balances precision and recall, ideal for imbalanced datasets. AUC-ROC: Evaluates the model’s ability to distinguish between classes (e.g., positive vs. negative). Step 6: Deploy and Monitor Deployment integrates the model into business workflows: API Integration: Embed the model into CRM systems or chatbots for real-time analysis. For example, a travel agency might flag negative tweets about flight delays and auto-respond with rebooking options. Cloud Deployment: Use platforms like AWS SageMaker or Google Vertex AI for scalable processing. Post-deployment, continuous monitoring is essential: Model Drift: Detects performance decay as language evolves (e.g., new slang like “mid” replacing “average”). Retraining: Use MLOps pipelines to auto-retrain models with fresh data monthly. Advanced Capabilities to Integrate While Building a Sentiment Analysis Tool When building an AI sentiment analysis tool, think beyond the foundational steps and focus on integrating advanced capabilities that enhance its functionality. In the previous section, we covered the core process of building the tool.  Here, we’ll discuss additional features and functionalities you can incorporate to make your sentiment analysis tool more powerful, versatile, and impactful. Enhanced Contextual Understanding Basic sentiment analysis can classify text as positive, negative, or neutral. However, adding enhanced contextual understanding helps interpret sarcasm, humor, and cultural nuances: Sarcasm Detection: Train the model to recognize sarcasm by analyzing tone, word choice, and context. For instance, a tweet like "Oh fantastic, another delayed flight!" should be flagged as negative sentiment despite using the positive word "fantastic." Idiomatic Expressions: Incorporate support for idioms and colloquial language that varies across regions and cultures. For instance, people use phrases like "It’s not my cup of tea" to convey specific meanings that others must understand correctly. Contextual Disambiguation: Teach the model to differentiate similar words based on context. For example, it could detect slang like "sick" and interpret its meaning as either illness (negative) or an impressive quality (positive sentiment), depending on the context.  Multilingual Support A sentiment analysis tool should handle multiple languages and dialects while considering cultural differences in sentiment expression, as it is essential for global businesses. Language Detection: Automatically detect the language of the input text and apply the appropriate sentiment analysis model. Cultural Differences: Train the model to recognize how sentiment is expressed differently across cultures.  Translation Integration: Use translation APIs (e.g., Google Translate or DeepL) to preprocess multilingual data before sentiment analysis, ensuring consistent results across languages. Manage, curate, and label multimodal AI data. Real-Time Analysis Businesses require real-time insights to quickly respond to customer feedback and trends. Adding real-time analysis enables your tool to: Monitor Social Media Feeds: Monitor references to your brand on platforms such as Twitter, Facebook, or Instagram in real time. This is particularly helpful for spotting viral complaints or trending topics. Analyze Live Customer Interactions: Process sentiment during live chats, phone calls, or video conferences to identify urgent issues or opportunities.  Trigger Alerts: Set up automated alerts for critical situations, such as a sudden increase in negative sentiment or a viral complaint.  Customizable Workflows Every business has unique needs. Hence, offering customizable workflows ensures your sentiment analysis tool can adapt to various use cases: Custom Labels: Allow users to define their own sentiment categories or labels based on specific requirements.  Rule-Based Overrides: Enable users to set rules for specific scenarios where the AI might struggle. For instance, flagging all mentions of a competitor’s product as "Neutral" regardless of sentiment. Integration Flexibility: Provide APIs and SDKs to integrate the tool seamlessly with existing systems, such as CRM platforms, social media dashboards, or customer support software. Customizability keeps the tool relevant and valuable across different industries and applications. Key Takeaways AI-powered sentiment analysis is a transformative approach to understanding customer emotions and opinions at scale. It augments traditional feedback analysis by offering scalability, consistency, and actionable insights while maintaining the flexibility for human oversight where needed. Below are some key points to remember when building and using sentiment analysis tools: Best Use Cases for Sentiment Analysis: Sentiment analysis is highly effective for monitoring brand reputation on social media, understanding customer feedback, improving support processes, and gathering market insights. It effectively identifies emotions, urgency, and trends as they happen. Challenges in Sentiment Analysis: Key challenges include tackling noisy data, understanding context like sarcasm and slang, ensuring support for multiple languages, and addressing biases in models. Addressing these challenges aims to develop equitable and reliable sentiment analysis tools. Encord for Sentiment Analysis: Encord’s advanced tools, including linguistic annotation and layered audio annotations, enhance the quality of training data. These tools also integrate with state-of-the-art models like GPT-4o and Whisper to streamline development.

What is data management? In today’s data-driven world, data management is the backbone of innovation, especially in artificial intelligence (AI). Data management refers to the systematic process of collecting, organizing, and maintaining data so that it is accessible, accurate, and secure to be used for a variety of tasks. Data management in AI involves processes such as data collection, storage, cleaning, annotation and review, curation, evaluation, monitor and integration. In the context of AI, data management plays an important role. The efficiency of AI systems largely depends upon the data on which it is trained. High-quality and well-organized data helps to build robust machine learning (ML) models. If data management is lacking, AI systems may be built on inconsistent, redundant, or biased information, resulting in inaccurate predictions and poor decision-making. This is why modern data management solutions now include advanced features such as automated data cleaning, versioning, metadata tracking, and real-time integration pipelines, all of which are essential for supporting dynamic AI workflows. Encord as a Data Management Tool (Source) Data management solutions are not simply used to store data and retrieve it. They also address several data related issues with the help of following features. Unifying Data Data management solutions combine data from various sources such as different databases, file systems etc. to create a single dataset. This makes sure that AI models are trained on complete data rather than incomplete and fragmented data. Data management systems allow the conversion of data into standardized formats and uniform naming conventions and schemas. It helps the centralization of dataset across teams working on building the AI models.  Breaking Down Silos Data management solutions break down silos by enabling different teams to share their data. The data management system uses automated pipelines to continuously update and merge data. This feature ensures that teams working on such data get the updated data. Metadata tracking and data catalogs features in the data management systems make it easy  to find and understand data from different sources. Solving Data Quality Problems Data management systems also solve data quality problems by automatically cleaning data and fixing errors with the help of smart algorithms. This ensures that the data for training AI models is accurate. It helps tracking changes made in the data over time to enable teams to compare versions of data, revert changes and run reproducible experiments. It also enforces security and governance policies to protect sensitive information and ensure compliance to build trust in AI outcomes. A good data management system not only improves the predictive accuracy of machine learning models but also facilitates faster innovation and better decision-making across the teams and organization. Types of Data Management Solutions There are different types of data management solutions available to handle a variety of tasks based on the specific requirement. Following are the list of some popular data management solutions. Database Management Systems (DBMS) DBMS is the most popular data management solution. DBMS can  store, retrieve, and manage structured data in a structured format (e.g., tables). For example, DBMS can store structured training data such as customer records, transaction logs, or sensor data for training AI models. It can be used to query and retrieve data quickly to build features for machine learning models. DBMS may serve as a backend for AI applications that require real-time data access (e.g., recommendation systems). DBMS can be used to store text for building NLP models and images to train Computer Vision models. Examples are MySQL, PostgreSQL, and Oracle that manage structured data using tables and SQL queries. No-SQL databases such as MongoDB are used to handle unstructured or semi-structured data. There are few other types of data stores such as key-value NoSQL database i.e. Redis, and graph databases i.e. Neo4j. Neo4j Desktop (Source) Data Warehouses Data Warehouses are centralized repositories for storing and analyzing large volumes of structured data from multiple sources. Data Warehouses are used for analytical processing. Data warehouses may combine data from multiple sources into a single repository optimized for query performance.They can be used to extract features or trends from data by running complex queries and analytical operations. These features then can be used to train AI models. They also support large-scale training processes by providing high-performance data access during feature engineering. Examples of data warehouses are Amazon Redshift, Google BigQuery, and Snowflake. Amazon Redshift (Source) Data Lakes Data lakes are systems that store large volumes of  raw, unstructured, semi-structured, and structured data. They can store different types of data such as logs, images, videos etc. It can be useful for big data analytics, machine learning, and other purposes where data schema are not well-defined. For training AI models, it can store raw, unstructured data such as images, videos, logs and also enable exploratory data analysis (EDA). For example, to build AI models for healthcare applications, data lakes such as  AWS S3 can be used to store and process medical images (X-rays, MRIs). Examples of data lakes are AWS S3, Azure Data Lake, Databricks. Azure Data Lake (Source) Master Data Management (MDM) Master Data Management is used to store single, consistent business data such as customer, product, or employee data. In the context of AI, MDM eliminates data silos and ensures data consistency and helps in improving data accuracy of training data. For example, an e-commerce company can use MDM to create a unified customer profile for building personalized recommendation systems. Examples of MDM are Informatica MDM, SAP Master Data Governance. AI-driven match and merge in Informatica MDM (Source) Challenges in Data Management AI models are generally trained on large amounts of data. There are many challenges in managing such data. The following are some of the common challenges: Data Quality and Consistency Performance of AI models depends upon high-quality data. Inconsistent, incomplete, or noisy data can lead to models that perform poorly or make biased predictions. For example, while building a computer vision project, if the collected images have different resolutions,  lighting conditions, and noise levels, the model may learn incorrect features. To overcome this, advanced data cleaning and normalization techniques should be applied to the images before training. Data Integration and Silos A dataset is often collected from multiple sources with different formats, structures, and standards. Integrating this variety of data into a single, coherent dataset is complex and time-consuming. For example, an AI system for predictive maintenance in manufacturing may require sensor data of different machines stored in different databases. The model may overlook correlations in the data if it is not properly integrated, which would lower its capacity to generate accurate predictions. Data Security and Privacy AI models are generally trained on sensitive data which can include personal information in healthcare or proprietary business data etc. Therefore the security and privacy for using such data must be ensured. For example, in medical imaging, patient X-rays must be handled with strict adherence to privacy laws such as HIPAA. Techniques like data anonymization and secure data storage solutions are important to prevent breaches and unauthorized access. Scalability and Volume AI models require large volumes of data for training. Managing and processing such large datasets (often in the terabyte to petabyte range) requires scalable storage, processing power, and efficient data pipelines. For example, a global retailer using AI for personalized recommendations must use vast amounts of customer interaction data to train accurate models and this data is appended in the data source from time to time. Without scalable solutions like cloud storage and real-time integration pipelines, the system may lag or fail to update the data promptly. Data Versioning and Traceability As the data set increases over time, keeping track of different versions of the data set and ensuring reproducibility of experiments becomes a challenging task. For example, in iterative model training for autonomous vehicles, it is important to maintain versions of the training dataset as road conditions change seasonally. Data version control tools help track these changes so that models can be re-trained or compared reliably. As these challenges directly affect the performance of AI models, a robust data management solution is needed.   Key Features in Data Management Tools Data management tools are essential in handling, processing, and preparing data for AI and machine learning projects. Below are some key features of these tools: Data Integration and Import Capabilities This is a must have feature for data management tools. Every data management tool must be able to allow users to integrate and import data from multiple sources (such as APIs, databases, cloud servers or even IoT devices) and formats into the project. For example, to build healthcare AI models, the patient records, lab results, and imaging data needs to be integrated into the project pipeline from different systems and sources. A data management tool must be able to connect to these systems and allow importing the data into the project. Importing Data from Multiple Sources in Encord (Source) Annotation Flexibility and Customization A data management system must also allow users to label, tag, or annotate data for supervised learning according to the specific use cases. For example, in an autonomous vehicle project the different objects in the images should be annotated with different labels or classes such as  pedestrians, traffic signs, and other vehicles. A data management tool with flexible annotation capabilities allows them to customize labels and annotation formats for different object types and use cases. Collaboration and Workflow Management A data management system must enable teams to collaborate on data preparation, annotation, and model training with role-based access and task tracking. This is essential as AI projects involve cross-functional teams (data scientists, engineers, domain experts). Therefore the efficient collaboration for such user types ensures faster and accurate outcomes. For example, in a large-scale AI project for retail analytics, a team might include data engineers, annotators, and domain experts. The collaboration in the data management system allows team members to work on different parts of the dataset concurrently, assign review tasks, and maintain version control over annotations. Quality Control and Validation Mechanisms Data management tools must be able to ensure accuracy, consistency, and completeness of data and annotations using automated checks, manual reviews, and anomaly detection. Poor-quality data leads to defective and faulty AI models. Therefore a quality control mechanism is required to  prevent errors from propagating through the pipeline. For example, in a computer vision project, a data management tool with quality control and validation mechanism will help in  automated quality checks that flags inconsistent annotations in image annotations, such as CT scan annotation in healthcare dataset. This helps radiologists reviewers to quickly review and correct any errors to ensure that the final dataset is reliable for training diagnostic AI models in healthcare. Identifying mislabelled annotation in Encord (Source) Scalability and Performance A data management platform must be able to handle large datasets and high-throughput workloads without compromising speed or reliability. Since AI projects often involve terabytes or petabytes of data, scalable tools ensure efficient processing and storage of such large data. For example, training a large language model (LLM) on billions of text documents requires a scalable data management tool with possibly distributed computing support, Cloud-native architecture for elastic scaling and optimized querying and indexing for fast data retrieval. Automation and AI-Assisted Annotation Data management tools should use AI to automate repetitive tasks like data labeling etc. Automation of such tasks reduces manual effort and speeds up tasks. The automated annotation is a must because manual annotation is time-consuming and expensive. AI-assisted annotation tools can improve efficiency and consistency of annotation tasks. Integration with Machine Learning Frameworks Data management tools should be compatible with popular ML frameworks such as TensorFlow, PyTorch, Scikit-learn for model training and deployment. Data management tools should integrate with the broader AI ecosystem to streamline workflows and allow data to be exported in formats that can be directly used for model training and testing. For example, a research lab working on deep learning models can export annotated datasets in TensorFlow or PyTorch compatible formats. This smooth integration accelerates the transition from data preparation to model development and evaluation. Data Visualization and Reporting A data management tool should offer reporting and visualization features that are easy to understand. It should include dashboards together with charts and reports to review data quality as well as model performance and annotation progress. The visualization of data helps in better understanding of trends present in the data to identify problems and make decisions. A data management tool should display real-time dashboards showing annotation progress alongside data distribution and error rates. The team gains insight through these findings which enables them to make accurate resource allocations and implement better processes. How Encord helps in Data Management Encord is a comprehensive data management platform for AI projects. It addresses the challenges of handling, annotating, and preparing data for AI workflows across multimultiple modalities. Below is a detailed explanation of how Encord’s features map to the essential functionalities needed for effective data management in AI: Encord AI Data Management Life Cycle (Source) Data Integration and Import Capabilities Encord supports seamless integration with various data sources, including cloud storage (AWS, Google Cloud, Azure), databases (such as Oracle), local data sources and OTC. It allows users to import different data types such as images, videos, text, etc. into a unified platform. Encord eliminates data silos, enabling teams to centralize and manage data from disparate sources efficiently. Data Integration in Encord from Different Sources (Source) Annotation Flexibility and Customization Encord provides a highly flexible and customizable annotation platform that supports a wide range of annotation types such as bounding boxes, polygons, keypoints, etc. for computer vision, natural language processing (NLP), and other types of AI projects. Encord ensures high-quality annotations for specific AI use cases that help in improving model accuracy and reducing manual effort. Keypoint Annotation in Encord (Source) Collaboration and Workflow Management Encord enables teams to work simultaneously on datasets. Multiple annotators, reviewers, and project managers can access the platform concurrently and get the real-time updates for the task. Built-in workflow management tools allows to assign specific tasks to team members and monitor annotation progress. This ensures that every stage of the data lifecycle is tracked which helps in maintaining high standards and meeting project deadlines. Workflow Management in Encord (Source) Quality Control and Validation Mechanisms Encord includes built-in quality control features to ensure the accuracy and consistency of data.  Encord has AI assisted validation processes to automatically flag inconsistencies or errors in annotations. This feature makes sure that no poor data enters the training pipeline. Encord also allows manual review cycles. Annotated data can be cross-checked by multiple experts to ensure that every label is accurate and reliable before being used in model training. Version control in Encord enables tracking and reviewing annotation histories. Summary Tab for Performance Monitoring (Source) Scalability and Performance Optimization Encord is built to handle large scale datasets. The cloud-native architecture of Encord ensures scalability and performance. Its architecture is designed to maintain speed and responsiveness even when data size increases. Encord helps in managing large datasets efficiently with features such as scalability, fast retrieval etc. Encord Active also helps to evaluate the performance of models based on different metrics. Evaluation Model Performance for Annotation Task (Source) Automation and AI-Assisted Annotation Encord supports AI assisted annotation to streamline the annotation process. This automated step can significantly reduce manual effort required for annotation and speed up the overall annotation process. Annotators correct or confirm AI-generated labels and the platform learns and improves these suggestions over time. This iterative cycle not only boosts efficiency but also increases the accuracy of your dataset. Encord Annotate offers high-quality annotation with automation capabilities using AI Agents to increase accuracy and speed. Automated Annotation in Encord using AI Agents (Source) Integration with Machine Learning Frameworks Once the data is annotated and quality-checked, it can be exported to various formats (such as COCO, Pascal VOC, or custom JSON formats) that are directly compatible with popular ML frameworks like TensorFlow and PyTorch. Encord bridges the gap between data management and model training within the AI development lifecycle. Exporting Labels from Annotation Projects (Source) Data Visualization and Reporting Encord provides visualization tools that let you monitor annotation progress, error rates, and overall project health in real time. Dashboards display key metrics that are essential for tracking performance and identifying areas for improvement. Encord generates detailed reports that offer insights into data distribution, annotation quality, and workflow efficiency. These reports can be used to inform decision-making and adjust strategies as needed. Data Visualization in Encord (Source) Get in-depth data management, visualization, search and granular curation with Encord Index. Key Takeaways: Data Management Solution Effective data management is important for building reliable and accurate machine learning models. A good data management system ensures data quality, consistency, and accessibility which as a result helps increase performance of AI. Importance of Data Management in AI: Proper data management ensures AI models are trained on high-quality, well-organized data. Poor data management can lead to inaccurate predictions and biased models. Key Features of a Data Management Solution: Data management solutions unify data from multiple sources, break down silos to assist collaboration, and perform automated data cleaning and quality control to ensure accuracy and reliability. Different Types of Data Management Solutions: There are different types of data management tools for specific needs, including DBMS for structured data, data warehouses for large-scale analytics, data lakes for raw and unstructured data, and MDM for maintaining consistency in business data. Challenges in Data Management for AI: Organizations must address data quality issues, integration complexities, security and privacy risks, scalability concerns, and the need for data versioning. Essential Features to Look for in Data Management Tools: AI-assisted data management tools should support data integration, flexible annotation, collaborative workflows, quality control mechanisms, scalability, and compatibility with ML frameworks like TensorFlow and PyTorch. How Encord Enhances AI Data Management: Encord supports data integration from different sources, AI assisted annotation, workflow management, quality control, scalability for large datasets, data export for various ML frameworks, and data visualization.

Autonomous Mobile Robots (AMRs) are changing how industries handle physical automation. Unlike traditional robots, which follow paths, AMRs use sensors and artificial intelligence (AI) to make decisions in real-time to navigate complex environments without human intervention. They are widely used in warehouses, manufacturing, healthcare, and many other industries where physical flexibility and efficiency matter. Businesses face challenges like multimodal data processing, system integration, and workforce adaptation when adopting AMRs.  This guide explains how AMRs work, their key differences from traditional mobile robots, and what companies must consider when deploying these automation solutions.  AMRs vs. Traditional Mobile Robots What Are Autonomous Mobile Robots? AMRs are self-navigating robots designed to move and operate in dynamic environments without predefined paths. They collect data from a combination of sensors, cameras, and LiDAR and use AI algorithms to understand the surroundings and make real-time navigation decisions. This makes them more flexible than robots that rely on fixed tracks, magnetic strips, or external guidance.  Key Differences from Automated Guided Vehicles (AGVs) Navigation: The traditional AGV robots follow fixed predefined routes, while AMRs dynamically adjust to obstacles and changing conditions and make their path. Decision-Making: AMRs use AI to make navigation and task decisions, reducing the need for direct human control, whereas traditional robotic systems heavily depend on humans. Scalability: AMRs can be deployed in existing facilities without significant infrastructure changes, but the AGVs often require dedicated pathways or modifications. Applications: AMRs are suited for environments where conditions frequently change, such as warehouses, hospitals, and retail stores. Traditional mobile robots are better for structured environments like assembly lines. Applications of AMRs Warehouse & Logistics AMRs are widely used in warehousing for order fulfillment and inventory transport. They efficiently move heavy loads and pallets between storage areas, reducing manual labor. For example, companies like Amazon use fleets of AMRs in their distribution centers to assist in the picking and sorting items in fulfillment centers, improving efficiency and accuracy. Source Manufacturing In manufacturing, AMRs handle material and transport payloads between production lines, delivering parts and tools where needed. These industrial robots also assist in assembly line support by ensuring the smooth flow of materials reducing downtime. Companies like Tesla use AMRs to move payloads efficiently around their assembly lines to streamline operations. Source Healthcare AMRs can autonomously deliver medical supplies, such as medicines and lab samples, to different departments within hospitals. For example, hospitals use AMRs to deliver patients their medication and food, improving safety and reducing contact during critical times. Retail In retail, AMRs are used for shelf scanning, inventory restocking, and customer assistance. Walmart has implemented AMRs for stock checking and inventory management, ensuring shelves are fully stocked and inventory is accurately tracked. Source Agriculture AMRs assist in precision farming by monitoring crops and autonomously harvesting them. For example, robotic harvesters can be used in orchards to pick fruit, reducing the need for human labor and increasing harvesting efficiency. How Do AMRs Work? AMRs are not just systems designed to do a particular physical task but possess a set of systems dedicated to observing and understanding the environment, processing real-time data, and determining the best course of action while avoiding obstacles. This section breaks down the core technical components that enable AMRs to function efficiently. Perception and Localization AMRs need to understand their surroundings to navigate safely and effectively. It uses a string of sensors to provide a continuous stream of data about their environment. Here are some of the key sensors used in AMRs are how they work: LiDAR (Light Detection and Ranging): LiDAR emits laser pulses to measure distances and create a high-resolution 3D map of the environment. It helps AMRs detect obstacles like walls, people, and other bots. Cameras: All types of camera visual, RGB, and depth cameras allow AMRs to recognize objects, signage, and even human movement patterns. Depth cameras help estimate distances and improve obstacle avoidance. IMU (Inertial Measurement Unit): The IMU consists of accelerometers and gyroscopes that track the AMR’s orientation, acceleration, and angular velocity. It helps control the motion of the AMR and stabilize the navigation. Ultrasonic and Infrared Sensors: These sensors help detect nearby objects in low visibility conditions where LiDAR and cameras may struggle, such as in foggy or low-light environments. GPS and RTK (Real-Time Kinematic): GPS provides general location data, while RTK checks on positioning accuracy, especially for outdoor AMR applications like agriculture and last-mile delivery. The data from all these sensors is used by the Simultaneous Localization and Mapping (SLAM) algorithms to build and continuously update a map or a layout of the surroundings while tracking the position of the AMR within it.  How SLAM Works The AMR collects the spatial data by continuously scanning its environment using LiDAR and cameras. Initially, the SLAM algorithm identifies key landmarks and reference points to establish positional awareness and creates a map. By comparing real-time sensor inputs with pre-existing maps or the new ones on the fly, SLAM helps the AMR to dynamically refine its understanding of the surroundings. This ongoing process allows the AMR to update its position relative to identified landmarks, ensuring precise navigation and adaptation to environmental changes. Source Navigation and Path Planning Once an AMR has localized itself within an environment, it must determine how to avoid obstacles while moving from point A to point B. This involves path planning and motion control algorithms. Here are some of the key path planning algorithms used in AMRs are how they work: A* (A-Star) Algorithm: This is a popular pathfinding algorithm which calculates the shortest path to a target while considering obstacles. Dijkstra’s Algorithm: This algorithm finds the shortest path by evaluating all possible routes. It is effective but computationally expensive. Rapidly-exploring Random Tree (RRT): This is useful for navigating highly dynamic environments with unpredictable obstacles. D* Lite Algorithm: An optimized version of the Dijkstra and A* algorithms, D* Lite is designed for dynamic path planning.  While executing a pre-planned path, AMRs must adjust their routes in real-time to avoid unexpected obstacles. This involves: Reactive Control: AMRs should immediately change direction when they detect an obstacle using proximity sensors and cameras. Predictive Modeling: The ML models help AMRs anticipate how objects like humans or forklifts may move and adjust accordingly. Dynamic Replanning: If an obstacle blocks the path, AMRs recalculate the optimal route using updated SLAM data. Artificial Intelligence Here are some of the ways AI algorithms plays a role in the mobile robots to make decisions and learn from the experience: Computer Vision for Object Recognition Machine learning models, including Convolutional Neural Networks (CNNs) help AMRs to identify and interpret the objects. Image segmentation improves their ability to categorize areas such as walkways, hazardous zones, and loading docks. The Optical Character Recognition (OCR) allows AMRs to decode labels, barcodes, and instructions, streamlining operations in warehouses and retail environments. Reinforcement Learning for Adaptive Behavior AMRs can use Reinforcement Learning (RL) to optimize their movement strategies by trial and error. Algorithms like Deep Q-Networks (DQN) help AMRs navigate efficiently without explicit pre-programming. RL allows AMRs to improve performance over time, learning from previous navigation experiences. Natural Language Processing (NLP) for Human Interaction Some AMRs are equipped with NLP capabilities to interpret voice commands and communicate with humans for seamless collaboration in industrial settings. Data Computing AMRs generate a huge amount of data which must be processed quickly for real-time decision making. This data is dandles using a combination of edge and cloud computing.  Edge Computing (On-Device Processing) Critical for real-time navigation and obstacle avoidance. Reduces latency by processing data locally instead of sending it to the cloud. Essential for safety applications where immediate responses are required. Cloud Processing Used for large-scale data analysis, optimization, and predictive maintenance. Enables AMRs to share data across fleets and improve coordination. Facilitates software updates, AI model training, and performance tracking. The AMRs use a combination of both. It processes essential data on the edge and uses the cloud for running the deep learning models and system wide improvements. Fleet Management and Coordination In many industries, AMRs are deployed in fleets, hence centralized coordination is necessary. Fleet management systems (FMS) assign tasks based on priority and availability using optimization algorithms. Real time monitoring helps to track performance and intervene when needed. The Vehicle to Vehicle (V2V) Communication helps AMRs to share data through wireless networks like WiFi,5G, or proprietary protocols. By exchanging the information on obstacles, routes, completed tasks, etc AMRs improve the coordination of the whole fleet. This ensures all the AMRs are able to operate efficiently. As we saw, AMRs use perception, navigation, AI, and fleet management to operate on their own. One of the key advantages of AMRs is their ability to handle tasks autonomously, reducing reliance on human workers for repetitive tasks or physically demanding jobs. However, their performance depends on how well they process large amounts of multimodal data. Managing this mix of data, such as LiDAR scans, camera feeds, and fleet coordination, is challenging and affects how well they scale, adapt, and function efficiently. Data Challenges of AMRs Multimodal Data Complexity AMRs rely on a combination of LiDAR, cameras, IMUs, and other sensors, each producing different types of data with varying formats and resolutions. Integrating and synchronizing these multimodal data streams in real-time is critical for accurate decision-making and needs robust processing architectures. Data Storage and Bandwidth Constraints Storing high-resolution LiDAR point clouds, video feeds, and telemetry data need significant storage resources. Transmitting this data between AMRs and cloud systems can also lead to bandwidth limitations, particularly in industrial environments with limited network infrastructure. Data Annotation and Labeling for AI Models Training algorithms for AMRs to recognize objects, classify environments, and predict movements requires large-scale, well-labeled datasets. However, annotating multimodal data can be time-consuming and labor intensive. Latency in Real-Time Processing For AMRs to react effectively to dynamic environments, data processing must happen with minimal latency. While edge computing helps process critical data locally, balancing edge and cloud processing remains a challenge to ensure operation without delays. Security and Privacy Concerns AMRs operating in sensitive environments, such as hospitals or warehouses, collect data that may contain proprietary or confidential information. Securing data transmission, storage, and compliance with regulations is a critical challenge. Scalability and Data Management for Fleets As organizations deploy fleets of AMRs, managing data across multiple robots becomes complex. Ensuring consistency, synchronizing updates, and analyzing fleet-wide performance require robust data management and orchestration strategies. Handling Data Challenges When data is distributed across different workflows, the decision making slows, the response times increase, and operational efficiency in general declines. A unified, integrated approach to data management is essential to overcome these challenges. This allows AMRs to operate with a near real-time understanding of their environment, improving navigation, coordination, and adaptability. Multimodal data management platforms help streamline AMR data processing by providing: Automated Data Labeling: Reducing manual annotation efforts for large multimodal datasets and curating balanced training dataset. Scalable Data Pipelines: This helps with data ingestion, synchronization, and processing. AI-Driven Insights: Delivering real-time analytics to improve AMR performance and fleet coordination. Key Considerations for Businesses Adopting AMRs Infrastructure: The physical environment plays an important role in the successful deployment of AMR technology. Businesses must ensure that the facilities can accommodate these autonomous robots with proper safety features, charging stations, navigation paths, and safe zones. Software Integration: AMRs must integrate seamlessly with existing systems like enterprise resource planning (ERP) systems and warehouse management systems (WMS). A smooth data flow between robots and software solutions is key for optimized operations. Cybersecurity Risks: With AMRs being connected to enterprise networks, businesses must address cybersecurity concerns. Protecting the robots and their data from potential cyber threats requires robust security protocols and constant monitoring. Training: To maximize the benefits of automation systems, businesses must provide training programs for employees who will interact with or oversee these robots. This includes safety training, technical skill development, and understanding the robots’ functionalities. Cost vs. Efficiency Trade-offs: While AMRs may require a significant initial investment, businesses should weigh this against the ongoing efficiency improvements and reduced labor costs they bring. It's essential to evaluate the total cost of ownership, including maintenance and upgrades, against potential operational savings to ensure long-term profitability. Conclusion The AMRs are transforming the industries as they provide flexible, intelligent automation without needing any major changes. Their ability to navigate dynamic environments, process multimodal data from advanced sensors, and operate autonomously make them ideal for warehouse operations, healthcare, manufacturing and more. In order to build a robust ARM, you need focus on multimodal data management, system integration and workforce adaptation to maximize its benefits. With recent advancements in AI and robotics, AMRs are a valuable asset across various industries offering cost-effective automation and adaptability to dynamic environments. 📘 Download our newest e-book, The rise of intelligent machines to learn more about implementing physical AI models.

The global supply chain is more complex today than ever, with increasing demand for speed, accuracy, and efficiency. Businesses must move goods faster, while also reducing costs, minimizing errors and optimizing logistics. Traditional supply chain operations mainly rely on manual tasks and legacy systems and therefore, struggle to keep up with increasing demands. Supply chain automation uses artificial intelligence (AI), robotics and data-driven systems to streamline operations from warehouse management to delivery. As the adoption of automation grows, the companies face new challenges, particularly in handling unstructured data and optimizing AI models for real world applications. In this blog, we will explore supply chain automation, the data challenges companies face and how physical AI is rapidly transforming a number of industries to become more efficient, cost-effective, and accurate.  Understanding Supply Chain Automation Supply chain automation refers to the use of AI and robotics to improve the efficiency in logistics, manufacturing, and distribution. By reducing manual intervention, businesses can improve speed, safety, accuracy and cost effectiveness. The automation can span across various stages like from real time inventory tracking to using robots to handle warehouse goods.  How Supply Chain Automation Solution Works? The automation in the supply chain process generally involves: Robotic Process Automation (RPA): Using bots to handle repetitive tasks like data entry, order processing, and invoice management. Decision Making: Machine learning models analyze supply and demand patterns, and help businesses make better inventory and logistics decisions. Computer Vision & Robotics: Robots sort, pick, and pack goods in warehouses with precision, reducing human labor. IoT & Real-Time Tracking: Smart sensors track shipments, monitor warehouse conditions, and provide real-time updates on goods in transit. Autonomous Vehicles & Drones: Self-driving trucks and drones transport goods efficiently. It reduces dependency on human drivers. Key Benefits of Supply Chain Automation Increased Efficiency & Speed Automation technologies work 24/7 without fatigue. It ensures faster processing times for tasks like order fulfillment, inventory management, and warehouse operations. Efficient robotic systems also reduce manual errors, leading to smoother logistics operations. Workforce Optimization Labor costs in warehousing and logistics are high, and staffing shortages can disrupt operations. Automation reduces reliance on manual labor for repetitive and physically demanding tasks, allowing human workers to focus on higher-value activities such as supervising AI-driven systems or handling exceptions. Also, automation helps businesses ensure safety for the workforce. Improved Accuracy & Reduced Errors Human errors in inventory tracking, order fulfillment, and logistics management can cause costly delays and stock discrepancies. AI-powered automation ensures precise data entry, accurate order picking, and real-time tracking, reducing mistakes across the supply chain. Scalability & Flexibility Automated systems can scale up or down based on demand fluctuations. For example, during peak seasons like Black Friday or holiday sales, AI-driven fulfillment centers can process higher volumes of orders without requiring additional workforce hiring. Better Decision Making With AI-powered analytics, businesses can predict demand, optimize inventory levels, and streamline logistics. This data-driven approach helps companies make faster, smarter decisions, improving overall supply chain management. Why Supply Chain Automation is Critical Today? The global supply chain has faced many unexpected challenges in recent years like pandemic-related disruptions, labor shortages, increasing e-commerce demand, and rising logistics costs. Companies that fail to automate risk falling behind competitors that use the efficiency of automation. By implementing automation, businesses can future-proof their supply chains, ensuring agility, reliability, and scalability in an increasingly complex global market. Applications of Supply Chain Automation This is really transforming the industries by optimizing operations across warehousing, logistics, transportation, and fulfilment. Here are some of the key applications: Automated Logistics Warehouses nowadays are becoming fully automated environments where robotic systems handle tasks that require significant labor. This includes: Automated Picking & Sorting: Automated conveyor systems manage inventory movement, increasing the speed of fulfillment. Inventory Tracking: IoT sensors, RFID tags, and computer vision continuously track stock levels in real-time, reducing errors. Automated Storage & Retrieval Systems (AS/RS): These systems use robotic shuttles and cranes to optimize space utilization and ensure fast, efficient retrieval of items. Dynamic Order Processing: AI algorithms prioritize orders based on urgency, demand, and supply chain constraints. Example Massive fulfilment centers like Amazon use robotic arms to sort, pick and package millions of products daily. It reduces the need for manual labor and increases efficiency. Autonomous Freight and Delivery The transportation and logistics sector is integrating AI to improve efficiency, reduce delivery times, and minimize operational costs. This includes: Autonomous Vehicles & Drones: Self-driving trucks and delivery drones are being deployed for delivering products to customers, reducing dependence on human drivers. Route Optimization: Machine learning algorithms analyze traffic, weather, and delivery schedules to optimize routes. This helps in cutting fuel costs and improving on-time deliveries. Smart Freight Tracking: GPS and IoT sensors provide real-time shipment tracking, improving transparency and security in logistics. Example FedEx and UPS are testing autonomous delivery vehicles and AI route planning to speed up shipments and optimize delivery networks. Quality Control and Inspection Given the volume of the products handled by businesses, using AI models for quality control and inspection of the products at least the first line of inspection can be helpful. Defect Detection: Computer vision systems inspect goods in real-time, and identify defects or damages before they reach customers. Automated Sorting & Rejection: Robotics handle product sorting, and make sure defective items are removed from the supply chain before shipment. Predictive Maintenance for Equipment: AI systems monitor warehouse machinery and fleet vehicles, detecting potential failures before they occur. Example The Tesla factories use real time defect detection systems during the manufacturing and packaging process. Demand Forecasting Predictive analytics is helping businesses make better and data driven decisions by utilixing the huge amounts of supply chain data. Some of the applications are: Predicting Demand Spikes: Machine learning models analyze historical data, seasonal trends, and market conditions to optimize stock levels. Preventing Stock Shortages and Overstocking: Automated inventory systems adjust product procurement according to real-time visibility based on demand forecasts. Dynamic Pricing Adjustments: Data driven insights allow businesses to adjust pricing dynamically based on supply and demand fluctuations. Example Walmart uses forecasting models for inventory management across its global supply chain. It also analyses local demographics and purchasing patterns for cost savings associated with excess inventory, prevent stockouts and in general improve customer satisfaction. Warehouse Automation This makes the warehouse operations faster, safer and more efficient by automating one of the most physically demanding tasks in the supply chain businesses. Source Some of the applications are: Automated Unloading and Loading: Traditional trailer unloading is labor-intensive and slow. The robots automate the process, increasing speed while reducing physical strain on workers. Labor Optimization: By automating repetitive tasks, warehouse workers can shift to supervisory and higher-value roles, improving overall operational efficiency. Robotic Picking & Sorting: The robots can handle package sorting and placement with CV and ML models to minimize errors and maximize efficiency. Example Pickle Robot uses robotic arms to automate trailer unloading and package sorting. The robots are able to handle various package sizes with precision ensuring safety for workers and the products equally.  Watch our full webinar with Pickle Robot: Data Challenges in Supply Chain Automation Supply chain automation relies heavily on AI, robotics, and real-time data processing to optimize operations. However, managing and utilizing supply chain data presents several challenges. From unstructured data inefficiencies to fragmented systems, these issues can slow down automation efforts and impact the decision making process. Unstructured Data Issues Supply chain data comes from various sources like video feeds, IoT sensors, GPS tracking, and robotic systems. Unlike structured databases, this data is unorganized, complex, and difficult to process using existing systems. But the AI models require structured, labeled datasets to function effectively, but supply chain environments generate raw, unstructured data that must be cleaned, annotated, and processed before use. Also, since the supply chain data sources vary so much, the data modalities also vary. Hence, a reliable data processing platform is essential which can handle different modalities. Example Surveillance cameras in warehouses capture footage of package movements, but extracting meaningful insights such as detecting misplaced items or predicting equipment failures requires advanced models trained on well annotated video data. Edge Cases & Variability Warehouses and logistics hubs are highly dynamic environments where AI systems must handle unexpected conditions, such as: Irregular package sizes and shapes that may not fit standard sorting models. Unstructured warehouse layouts where items are moved manually, making tracking difficult. Environmental factors like poor lighting, dust, or obstructions that can impact AI vision systems. Example A robotic arm needs to be trained to pick all different shapes and sizes of boxes. Otherwise the arms would pick uniformly shaped boxes and may struggle when faced with irregular or damaged packages, leading to errors and delays. Lack of High-Quality Labeled Data Training AI models for supply chain automation requires large volumes of accurately labeled data. A process that is both time-consuming and expensive. Data annotation for robotics and computer vision requires human expertise to label objects in warehouse environments e.g., differentiating between package types, identifying conveyor belt anomalies, or classifying damaged goods.Without high-quality annotated datasets, AI models struggle with real-world deployment due to poor generalization. Example A self-driving forklift needs detailed labeled data of warehouse pathways, obstacles, and human movement patterns to navigate safely—without this, its performance remains unreliable. Data Silos and Fragmentation Supply chain data is often stored in disconnected systems across different departments, vendors, and third-party logistics providers, making it difficult to get a unified view of operations. Example A warehouse may use one system for inventory tracking, another for shipment logistics, and a separate platform for robotic operations. Without integrating and connecting all of these systems, AI models cannot make real-time, data-driven decisions across the entire supply chain. Improving Data for Effective Supply Chain Automation High quality data helps build reliable AI models which is essential in supply chain automation. From unstructured data processing to better annotation workflows and system integration, improving data quality can significantly improve AI logistics. Structuring Unstructured Data The data in the supply chain pipeline comes from various sources and in large amounts. It is mainly unstructured and needs to be processed, annotated and in general converted into a usable format so that AI models can be trained on it. This will help the AI models to make accurate and automate the process.  Comprehensive data platforms like Encord help organize, label and extract valuable insights from video or sensor data. Handling Edge Cases AI models must adapt to unexpected warehouse conditions such as damaged packages, irregular stacking, or poor lighting. During data curation for building automated supply chain models, it is essential to curate a diverse and well balanced dataset. Annotation tools allow the teams to label complex scenarios and also visualize the whole dataset and help curate a balanced training data. Efficient Data Annotation AI models for supply chain automation need large, high-quality labeled datasets, but manual annotation is slow and costly. AI-assisted annotation speeds up labeling while ensuring accuracy. Data platforms like Encord help identify, label, and visualize warehouse data, enabling teams to curate balanced training datasets for improved AI performance. Accurately label and curate physical AI data to supercharge robotics with Encord. Learn how Encord can transform your Physical AI data pipelines.   Conclusion Supply chain automation is revolutionizing how businesses manage logistics, warehouses, and transportation. AI, robotics, and real-time data analytics are improving the customer experience. However, bottlenecks such as unstructured data, edge cases, and fragmented systems must be addressed to access the automation’s full potential. High-quality, structured data is essential for training reliable AI models. Advanced annotation tools and intelligent data management solutions streamline data labeling, improve model accuracy, and ensure seamless system integration. With the use of these data platforms like Encord, business processes can build smarter, more scalable automation tools for supply chains. As automation adoption continues to grow, companies that effectively manage their data and AI workflows will gain a competitive edge. Future-ready supply chains will not only optimize efficiency but also enhance resilience, adaptability, and overall decision-making. To learn how to overcome key data-related issues when developing physical AI and critical data management practices, download our Robotics e-book: The rise of intelligent machines.