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Imagine a busy factory, where all the machines are running and sensors are tracking every detail of how they run. The key technology of this factory is a digital twin, a virtual copy of the whole factory. Meet Alex, the plant manager, who starts his day by checking the digital twin of the factory on his tablet. In this virtual model, every conveyor belt, robotic arm, and assembly station is shown in real time. This digital replica is not just a static image. It is a dynamic, live model that replicates exactly what is happening within the factory. Earlier in the week, a small vibration anomaly was detected on one of the robotic arms. In the digital twin, Alex saw the warning signals and quickly zoomed in on the problem area. By comparing the current data with historical trends stored in the model, the system predicted that the robotic arm might experience a minor malfunction in the next few days if not serviced. Alex then called a meeting with the maintenance team using the insights from the digital twin. The team planned a repair to ensure minimal disruption to production. The digital twin not only helped predict the issue but also allowed the team to simulate different repair scenarios and choose the most efficient one without stopping the production line. As production increases, the digital twin continues to act as a silent guardian monitoring energy use, optimizing machine settings, and suggesting improvements to reduce waste. It is like having a virtual copy of the factory in the cloud that constantly learns and adapts to make the physical world more efficient. Digital Twin in Factory (Source) What is a Digital Twin? A Digital Twin is a virtual representation of a physical object, system, or process that reflects its real-world version in real-time or near-real-time. It uses data from sensors, IoT devices, or other sources to simulate, monitor, and analyze the behavior, performance, or condition of the physical entity. This concept is widely used in industries like manufacturing, healthcare, urban planning, and more to help improve decision-making, predictive maintenance, and optimization. Digital twin fundamental technologies (Source) A Digital Twin is a dynamic, digital copy that grows and changes along with its physical counterpart. It combines data (whether from the past, in real-time, or predictive) with advanced technologies like AI, machine learning, and simulation tools. This allows it to provide insights, predict outcomes, or test scenarios without the need to directly interact with the physical object or system. A Digital Twin arrangement in automotive industry (Source) Types of Digital Twins Digital twins can be categorized into different types based on the scope, complexity of what they represent and application it can perform. Here are four primary types. Component Twins Component twins are digital replicas of individual parts or components of a larger system. They focus on the specific characteristics and performance metrics of a single element. For example, imagine a jet engine where each turbine blade is modeled as a component twin. By tracking stress, temperature, and wear in real time, engineers can predict when a blade might fail and schedule maintenance before a critical issue occurs. Asset Twins Asset twins represent entire machines or physical assets. They integrate data from multiple components to provide a collective view of an asset's performance, condition, and operational history. Consider an industrial robot on a production line. Its digital twin includes data from all its moving parts, sensors, and control systems. This asset twin helps the maintenance team monitor the robot’s overall health, optimize its performance, and schedule repairs to avoid downtime. System Twins System twins extend beyond individual assets to represent a collection of machines or subsystems that interact with one another. They are used to analyze complex interactions and optimize performance at a broader scale. In a smart factory, a system twin might represent the entire production line. It integrates data from various machines, such as conveyors, robots, and quality control systems. This comprehensive model enables managers to optimize workflow, balance loads, and reduce bottlenecks throughout the entire manufacturing process. Process Twins Process twins model entire workflows or operational processes. They capture not just physical assets but also the sequence of operations, decision points, and external variables affecting the process. A supply chain process twin could represent the journey of a product from raw material sourcing to final delivery. By simulating logistics, inventory levels, and transportation routes, businesses can identify potential disruptions, optimize delivery schedules, and enhance overall supply chain efficiency. Levels of Digital Twins Digital twins evolve over time as they incorporate more data, analysis, and autonomous capabilities. Here are the 5 Levels of Digital Twins. Descriptive Digital Twin A descriptive digital twin is a basic digital replica that mirrors the current state of a physical asset. It represents real-time data and static properties without much analysis. The example of a descriptive digital twin is a digital model of a hospital MRI machine that displays its operating status, temperature, and usage statistics. It shows the current condition but does not analyze trends or predict future issues. Diagnostic Digital Twin This level enhances the descriptive twin by adding diagnostic capabilities. It analyzes data to identify deviations, errors, or early signs of malfunction. For example, consider the same MRI machine that now includes sensors and analytics that detect if its cooling system is underperforming. Alerts are generated when operating parameters deviate from normal ranges to enable identification of the issue early. Predictive Digital Twin At this stage, the digital twin uses historical and real-time data to forecast future conditions. Predictive analytics help anticipate failures or performance drops before they occur. For a surgical robot, the predictive digital twin analyzes past performance data to predict when a component might fail. This allows maintenance to be scheduled proactively which reduces the risk of unexpected downtime during critical operations. Prescriptive Digital Twin It is a more advanced twin that goes beyond prediction to recommend specific actions or solutions, often with “what-if” scenario testing. It combines predictive insights with recommendations or automated adjustments. A digital twin of a hospital’s intensive care unit (ICU) monitors various devices and patient parameters. If the twin predicts a rise in patient load, it might suggest reallocating resources or adjusting ventilator settings to optimize care which ensures the unit runs smoothly during peak times. Autonomous Digital Twin It is the most advanced level of digital twins. An autonomous digital twin not only predicts and prescribes actions but can execute them automatically in real time. It uses AI and machine learning to adapt continuously without human intervention. For example, in a fully automated pharmacy system this digital twin monitors medication dispensing, inventory levels, and patient prescriptions. When it detects discrepancies or low stock, it autonomously reorders supplies and adjusts dispensing algorithms to ensure optimal service without waiting for manual input. Do Digital Twins Use AI? Digital twins often integrate AI to transform raw data into actionable insights, optimize performance, and automate operations. The following points describe how AI enhances digital twin models: Predictive Insights AI algorithms analyze historical and real-time data gathered by the digital twin to identify patterns and trends. For example, machine learning models can predict when a critical component in a manufacturing line might fail which enables maintenance to be scheduled proactively. By continuously monitoring performance metrics, AI can detect anomalies before they rise into major issues. This early detection helps prevent costly downtime and improves overall reliability. Advanced Analytics AI can analyze huge amounts of data from sensors to find hidden patterns and insights that traditional methods might miss. This deep analysis helps create more accurate models of how physical systems work. Advanced algorithms can also simulate different operating situations to let decision-makers test possible changes in a virtual setting. This is especially useful for improving system performance without causing real-world problems. Automation Using AI, digital twins can not only suggest corrective actions but also execute them automatically. For example, if a digital twin identifies that a machine is overheating, it might automatically adjust operating parameters or shut the machine down to prevent damage. AI models embedded within digital twins continuously learn from new data. This adaptability means that the system improves its predictive and diagnostic accuracy over time and becomes more effective in managing complex operations. Imagine a virtual copy of a factory production line. AI tools built into this virtual copy keep an eye on how well the machines are working. If the AI notices a small sign that an important part is wearing out, it can predict that the part might fail soon. The system then changes the workflow to reduce any problems, plans a maintenance check, and gives the repair team detailed information about what’s wrong. By using digital twin technology with AI, industries can move from reactive to proactive management and transform how they maintain systems, predict issues, and optimize operations. Digital Twins Examples Digital twins have many use cases in different domains. Let’s discuss some example of digital twins. Digital Twin in Spinal Surgery A digital twin in spinal surgery is a detailed virtual replica of a real surgical operation. It captures both the static setup (like the operating room and patient anatomy) and the dynamic actions (like the surgeon’s movements and tool tracking) in one coherent 3D model. A digital twin is a virtual simulation that mirrors an actual surgery, created by merging data from various sensors and imaging methods. Digital photograph of a spinal surgery (left) and rendering of its digital twin (right) (Source) Following are the main components of this digital twin system. Reference Frame: A high-precision 3D map of the operating room is built using multiple laser scans. Markers are placed in the room to fuse these scans into one common coordinate system. Static Models: The operating room, equipment, and patient anatomy are modeled using photogrammetry (detailed photos) and 3D modeling software. This produces realistic textures and accurate dimensions. Dynamic Elements: Multiple ceiling-mounted RGB-D cameras capture the surgeon’s movements. An infrared stereo camera tracks the surgical instruments with marker-based tracking. Data Fusion and Integration: All captured data is registered into the same reference frame, ensuring that every element—from the static room to dynamic tools, is accurately aligned. The system is built in a modular and explicit manner, where each component is separate yet integrated. Use of AI: AI techniques enhance dynamic pose estimation (e.g., using models like SMPL-H) and help in processing the sensor data. The detailed digital twin data also provides a rich source for training machine learning models to improve surgical planning and even automate certain tasks. Comparison of the rendered digital twin with the real camera images (Source) This digital twin can help in the following tasks: Training & Education: Surgeons and students can practice procedures in a risk-free, realistic environment. Surgical Planning: Doctors can simulate and plan complex surgeries ahead of time. Automation & AI: The rich, detailed data can train AI systems to assist with surgical navigation, process optimization, and even automate some tasks. The digital twin for spinal surgery is a comprehensive 3D virtual model that integrates high-precision laser scans, photogrammetry, multiple RGB-D cameras, and marker-based tracking. This system captures the entire surgical scene and aligns them within a common reference frame. AI plays a role in enhancing dynamic data capture and processing, and the detailed model serves as a powerful tool for training, surgical planning, and automation. Digital twin in Autonomous Driving This paper on digital twins in virtual reality describes a digital twin built in a virtual reality setting to study human-vehicle interactions at a crosswalk. The digital twin recreates a real-world crosswalk and an autonomous vehicle using georeferenced maps and the CARLA simulator. Real pedestrians interact with this virtual environment through a VR interface, where an external HMI (GRAIL) on the vehicle provides explicit communication (e.g., changing colors to signal stopping). The system tests different braking profiles (gentle versus aggressive) to observe their impact on pedestrian confidence and crossing behavior. The setup uses questionnaires and sensor-based measurements to collect data, and it hints at leveraging AI for data processing and analysis. Overall, this approach offers a controlled, safe, and realistic way to evaluate and improve communication strategies for autonomous vehicles, potentially enhancing road safety. Following are the components of the system. Digital twin for human-vehicle interaction in autonomous driving. Virtual (left) and real (right) setting (Source) Digital Twin Environment: The virtual crosswalk is digitally recreated using map data to ensure it matches the real-world layout. Experiments run in CARLA, an open-source simulator that creates realistic traffic scenarios. Human-Vehicle Interaction Interface: A colored bar on the vehicle indicates if the car is about to stop or yield. Two braking styles are tested which are gentle (slow deceleration) and aggressive (sudden deceleration). Virtual Reality Setup: Participants use a VR headset and motion capture to see and interact with the virtual world. Their movements are synchronized with the simulation for accurate feedback. Data Collection & Analysis: Participants share their feelings about safety and the vehicle's actions. The system records objective data like distance, speed, and time-to-collision. Role of AI: AI analyzes both subjective feedback and sensor data to model behavior and refine communication. AI helps integrate data so the simulation responds realistically to both the vehicle and pedestrians. This digital twin system helps in following: Enhances Safety: Clear communication through the digital twin helps pedestrians understand vehicle intentions, reducing uncertainty and potential accidents. Improves Training: It offers a realistic simulation for both pedestrians and autonomous vehicles, enabling safer, hands-on training and evaluation. Informs Design: By collecting both subjective feedback and objective measurements, designers can refine vehicle behavior and HMI features for better user interaction. Supports Data-Driven Decisions: The system’s real-time data and AI processing allow for continuous improvements in autonomous driving and pedestrian safety strategies. How Encord Enhances Digital Twin Models Encord, a data management and annotation platform which can be used in digital twin applications. It is used to annotate, curate, and monitor large-scale datasets to train machine learning models for digital twin creation and optimization. Following are the important points how Encord helps in creating and enhancing Digital Twins. Encord provides tools for preparing the data needed to train machine learning models that can power digital twins.  Encord allows users to annotate and curate large datasets, ensuring the data is clean, accurate, and suitable for training machine learning models that will be used in digital twin applications.  Encord platform enables users to monitor the performance of their machine learning models and datasets, allowing for continuous improvement and optimization of the digital twin.  By using high-quality, well-curated datasets, machine learning models can achieve higher accuracy and reliability.  Encord platform can accelerate the development of digital twins by streamlining the data preparation and model training process.  Digital twins powered by machine learning models can provide valuable insights into the performance of physical systems, enabling better decision-making. Key Takeaways Digital twin technology revolutionizes industrial operations by creating a dynamic virtual replica of physical systems. This technology not only mirrors real-time activities in environments like factories and hospitals but also uses historical data and AI to predict issues, simulate repairs, and optimize processes across various industries. Real-Time Monitoring & Visualization: Digital twins provide live, interactive models that replicate every detail of a physical system that allows to quickly identify anomalies and monitor system performance continuously. Predictive Maintenance: Digital twin helps in analyzing historical and real-time data which can be used to forecast potential equipment failures and enables proactive maintenance. Enhanced Decision-Making Through Simulation: Digital twins allow to simulate repair scenarios and operational adjustments in a virtual space which ensures the most efficient solutions are chosen. Cross-Industry Applications: From factory production lines to surgical procedures and autonomous driving, digital twins are transforming how industries plan, train, and optimize their systems. AI Driven Insights: The integration of AI and machine learning empowers digital twins to offer advanced analytics, automate corrective actions, and continuously learn from new data to improve accuracy over time.

At Encord, we believe in empowering employees to shape their own careers. The company fosters a culture of ‘trust and autonomy’, which encourages people to think creatively and outside the box when approaching challenges. We strongly believe that people are the pillars of the company. With employees from over 20 nationalities, we are committed to building a culture that supports and celebrates diversity. For us, we want our people to be their authentic self at work and be driven to take Encord's mission forward.  Rad Ploshtakov was the first employee at Encord and is a testament to how quickly you can progress in a startup. He joined as a Founding Engineer after working as a Software Engineer in the finance industry, and is now our Head of Engineering. Hi Rad! Tell us about yourself, how you ended up in Encord, and what you’re doing. I was born and raised in Bulgaria. I moved to the UK to study a masters in Computing (Artificial Intelligence and Machine Learning) at Imperial College London. I am also a former competitive mathematician and worked in trading as a developer, building systems that operate in single digit microseconds. Then I joined Encord (or Cord, which is how we were known at the time!) as the first hire - I thought the space was really exciting, and Eric and Ulrik are an exceptional duo. I started off as a Founding Engineer and, as our team grew, transitioned to Head of Engineering about a year later. I am responsible for ensuring that as an engineering team we're working on what matters most for our current and future customers - I work closely with everyone to set the overall direction and incorporate values for the team. Nowadays, a lot of my time is also spent on hiring, and on helping build and maintain an environment in which everyone can do their best work. What does a normal day at work look like for you? Working in a startup means no two days are the same! Generally, I would say that my day revolves around translating the co-founders' goals into actionable items for our team to work on - communicating and providing guidance are two important aspects of my role. A typical day includes meeting with customers and prospects, code reviewing, and supporting across different initiatives. Another big part is collaborating with other teams to understand what we want to build and how we are going to build it. Can you tell us a bit about a project that you are currently working on? Broadly speaking, a lot of my last few weeks has been supporting our teams as they set out and execute on their roadmaps. 2023 will be a huge year for us at Encord, and we're moving at a very fast pace, so a lot of my focus recently has been helping us be set up for success. As for specific projects, I'm very excited about all the work our team is doing for our customers. For example, our DICOM annotation tool has recently been named the leading medical imaging annotation tool on the market - which is a huge testament to the work our team has poured into it over the last year. I remember hacking together a first version of our DICOM annotation tool in my first (admittedly mostly sleepless!) weeks at Encord, and seeing how far it's come in just a few months has been one of the most rewarding parts of my last year. What stood out to you about the Encord team when you joined? Many things. When I first met the co-founders (Eric & Ulrik), I was impressed by their unique insights into the challenges that lay ahead for computer vision teams - they can simultaneously visualize strikingly clearly what the next decade will look like, while also being able to execute at mind-boggling speed in the moment, in that direction. I was impressed also by how smart, resourceful and driven they were. By the time I joined, they had been able to build a revenue generating business with dozens of customers - getting to understand deeply the problems that teams were facing and then iterating quickly to build solutions that not even they had thought about. What is it like to work at Encord now? It's a very exciting time to be at Encord. Our customer base has been scaling rapidly, and the feedback loop on the engineering cycle is very short, so we get to see the impact of our work at a very quick pace which is exciting - often going from building specs for a feature, to shipping it, showing it to our customers, and seeing them starting to use it all happen in a span of just a few weeks. A big part of working at Encord is focusing a lot on our customer's success - we always seek out feedback, listen, and apply first principles to the challenges our customers are facing (as well as getting ahead with ones we know they'll be facing soon that they might not be thinking about yet!). Then work on making the product better and better each day. How would you describe the team at Encord now? The best at what they do - also hardworking, very collaborative and always helping and motivating each other. One of our core values is having a growth mentality, and each member of our team has come into the company and built things from the ground up. Everyone has a willingness to roll up their sleeves and make things happen to grow the company. A resulting factor of this is also that it's okay to make mistakes - we are constantly iterating and trying to get 1% better each day. We have big plans for 2023 & are hiring across all teams! ➡️ Click here to see our open positions

Learn more about life at Encord from our Product Design Lead, Mavis Lok! Mavis Lok, or ‘Figma Queen’ as we’d like to call her, thrives in using innovation and creativity to enhance the user experience (UX) and user interface (UI) of our products. She listens closely to our customers’ needs, conducts user discovery, and translates insights into tangible and elegant solutions. You will find Mavis collaborating with various teams at Encord (from the Sales and Customer Success teams, to the Product and Engineering teams) to ensure that the product aligns with our business goals and user needs. Hi, Mavis, first question is what inspired you to join Encord? When I was planning the next steps in my career, I knew that I wanted to join an emerging and innovative tech startup. In the process, I stumbled upon Encord - with a pretty big vision of helping companies build better AI models with quality data. A problem that seemed ambitious and compelling.  I had my first chat with Justin [Encord's Head of Product Engineering], and he gave me great insights into the role, the company, and the domain space, which tied nicely with my design experience and what I was looking for in my next role. I was evaluating many companies, and I made sure (and I'd recommend to anyone reading!) to speak to as many employees from the company I could meet. The more people I met from Encord, the more and more eager I became to join the team. Could you tell me a little about what inspired you to pursue a career in product design? Hah, great question! I was previously in creative advertising and was trained as a Creative/Art Director. During my free time, I would participate in advertising competitions where I would pitch ideas for brands, and I’d always maximize my design potential through digital-led ideas. That brought me to work as a Digital Designer and then as a Design Manager, where I got my first glimpse of what it was like to work closely with co-founders, engineers, and designers. The company I was working at, was going through a transition from an agency to a SaaS type business model, and I found many of the skills I'd developed were actually an edge for what product design requires. Having an impact in balancing business needs, and product development challenges whilst creating products that are user-centric and delightful to use - is why I love what I do every day. How would you describe the company culture? I think the people at Encord are what sets us apart. With a team of over 20 nationalities, it’s an incredible feeling to work in an environment where diversity of thought is encouraged.  The grit, ambition, vision, and thoughtfulness of the team are why I enjoy being part of Encord.   What have been some of the highlights of working at Encord? Encord has given me the space to throw light on the impact that design can bring to the company and build more meaningful relationships with the team and, of course, our customers. Another big highlight for me is practicing the notion of coming up with ideas rapidly whilst being able to identify the consequences of every design decision. Brainstorming creativity whilst critically is something I hold dearly in my creative/design life, so it’s definitely a highlight of my day-to-day at Encord. On a side note, Encord is also a fun place to work. Whether it is Friday lunches, monthly social activities, or company off-sites, there are plenty of opportunities to have a good time with the team.  ​​Lastly, what advice would you give someone considering joining Encord? The first thing I would say is you have to be authentic during the interview, and you should also genuinely care about the mission of the company because there is a lot of buzz around the AI space right now - genuine interest lasts longer than hype. I would recommend reading our blogs on the website; it's a great place to start, as you can gain a lot of insight from it. From learning more about our customers, to exploring where our space is headed. We have big plans for 2023 & are hiring across all teams. Find here the roles we are hiring for.

For today’s version of “Behind the Enc-urtain”, we sat down with Shivant, Technical CSM at Encord, to learn more about his journey and day-to-day role. Shivant joined the GTM team when it was a little more than a 10 person task force, and has played a pivotal role in our hypergrowth over the last year. In this post, we’ll learn more about the camaraderie he shares with the team, what culture at Encord is like, and the thrill of working on some pretty fascinating projects with some of today’s AI leaders.  To start us off - could you introduce yourself to the readers, & share more about your journey to Encord? Of course!  I’m originally from South Africa – I studied Business Science and Information Systems, and started my career at one of the leading advisory firms in Cape Town. As a Data Scientist, I worked on everything from technology risk assessments to developing models for lenders around the world. I had a great time - and learned a ton! In 2022 I was presented the opportunity to join a newly-launched program in Analytics at London Business School, one of the best Graduate schools in the world. I decided to pack up my life (quite literally!) and move to London. That year was an insane adventure – and I didn’t know at the time but it prepared me extremely well for what my role post-LBS would be like. It was an extremely diverse and international environment, courses were ever-changing and a good level of challenging, and, as the cliche goes, I met some of my now-best friends! I went to a networking event in the spring, where I met probably two dozen startups that were hiring – I think I walked around basically every booth, and actually missed the Encord one. [NB: it was in a remote corner!]  As I was leaving I saw Lavanya [People Associate at Encord] and Nikolaj [Product Manager at Encord] packing up the booth. We started chatting and fast forward to today… here we are! What was something you found surprising about Encord when you joined? How closely everyone works together. I still remember my first day – my desk-neighbors were Justin [Head of Product Engineering], Eric [Co-founder & CEO] and Rad [Head of Engineering]. Coming from a 5,000 employee organization, I already found that insane! Then throughout the day, AEs or BDRs would pass by and chat about a conversation they had just had with a prospect – and engineers sitting nearby would chip in with relevant features they were working on, or ask questions about how prospects were using our product. It all felt quite surreal. I now realize we operate with extremely fast and tight feedback loops and everyone generally gets exposure to every other area of the company – it’s one of the reasons we’ve been able to grow and move as fast as we have. What’s your favorite part of being a Technical CSM at Encord? The incredibly inspiring projects I get to help our customers work on. When most people think about AI today they mostly think about ChatGPT but, beyond LLMs, companies are working on truly incredible products that are improving so many areas of society. To give an example – on any given day, my morning might start with helping the CTO of a generative AI scale-up improve their text-to-video model, be followed by a call with an AI team at a drone startup who is trying to more accurately detect carbon emissions in a forest, and end with meeting a data engineering team at a large healthcare org who’s working on deploying a more automated abnormality-detector for MRI scans.  I can’t really think of any other role where I’d be exposed to so much of “the future”. It’s extremely fun.  What words would you use to describe the Encord culture? Open and collaborative. We’re one team, and the default for everyone is always to focus on getting to the best outcome for Encord and our customers. Also, agile: the AI space we’re in is moving fast, and we’re able to stay ahead of it all and incorporate cutting-edge technologies into our platform to help our customers – sometimes a few days from it being released by Meta or OpenAI. And then definitely diverse: we’re 60 employees, from 34 different nationalities, which is incredibly cool. I appreciate being surrounded by people from different backgrounds, it helps me see things in ways I wouldn’t otherwise, and has definitely challenged a lot of what I thought was the norm.  What are you most excited re. Encord or the CS team this year?  There’s a lot to be excited about – this will be a huge year for us. We recently opened our San Francisco office to be closer to many of our customers, so I’m extra excited about having a true Encord base in the Bay area and getting to see everyone more regularly in person.  We’re also going to grow the CS team past Fred & I for the first time! We’re looking for both Technical CSMs and Senior CSMs to join the team, both in London and in SF, as well as Customer Support Engineers and ML Solutions Engineers. On the topic of hiring… who do you think Encord would be the right fit for? Who would enjoy Encord the most? In my experience, people who enjoy Encord the most have a strong sense of self-initiative and ambition – they want to achieve big, important outcomes but also realize most of the work to get there is extremely unglamorous and requires no task being “beneath” them. They tend to always approach a problem with the intent of finding a way to get to the solution, and generally get energy from learning and being surrounded by other talented, extremely smart people. Relentlessness is definitely a trait that we all share at Encord. A lot of our team is made up of previous founders, I think that says a lot about our culture.  See you at the next episode of “Behind the Enc-urtain”! And as always, you can find our careers page here😉

Artificial intelligence (AI) in robotics defines new ways organizations can use machines to optimize operations. According to a McKinsey report, AI-powered automation could boost global productivity by up to 1.4% annually, with sectors like manufacturing, healthcare, and logistics seeing the most significant transformation.  However, integrating AI into robotics requires overcoming challenges related to data limitations and ethical concerns. Also, the lack of diverse datasets for domain-specific environments makes it difficult to train effective AI models for robotic applications.  In this post, we will explore how AI is transforming robotic automation, its applications, challenges, and future potential. We will also see how Encord can help address issues in developing scalable AI-based robotic systems. Difference between AI and Robotics Artificial Intelligence (AI) and robotics are different yet interconnected fields within engineering and technology. Robotics focuses on designing and building machines capable of performing physical tasks, while AI enables these machines to perceive, learn, and make intelligent decisions.  AI consists of algorithms that enable machines to analyze data, recognize patterns, and make decisions without explicit programming. It uses techniques like natural language processing (NLP) and computer vision (CV) to allow machines to perform complex tasks.  For instance, AI powers everyday technologies, such as Google's search algorithms, re-ranking systems, and conversational chatbots like Gemini and ChatGPT by OpenAI.  Robotics, however, focuses on designing, building, and operating programmable physical systems that can work independently or with minimal human assistance. These systems use sensors to gather information and may follow programmed instructions to move, pick up objects, or communicate. A line following robot The integration of AI with robotic systems helps them perceive their environment, plan actions, and control their physical components to achieve specific objectives, such as navigation, object manipulation, or autonomous decision-making. Why is AI Important for Robotics? AI-powered robotic systems can learn from data, recognize patterns, and make intelligent decisions without requiring repetitive programming. Here are some key benefits of using AI in robotics: Enhanced Autonomy and Decision-Making Traditional robots use rule-based programs that limit their flexibility and adaptability. AI-driven robots analyze their environment, assess different scenarios, and make real-time decisions without human intervention.  Improved Perception and Interaction AI improves a robot's ability to perceive and interact with its surroundings. NLP, CV, and sensor fusion enable robots to recognize objects, speech, and human emotions. For example, AI-powered service robots in healthcare can identify patients, understand spoken instructions, and detect emotions through facial expressions and tone of voice. Learning and Adaptation AI-based robotic systems can learn from experience using machine learning (ML) and deep learning (DL) technologies. They can analyze real-time data, identify patterns, and refine their actions over time.  Faster Data Processing The modern robotic system relies on sensors such as cameras, LiDAR, radar, and motion detectors to perceive their surroundings. Processing such diverse data types simultaneously is cumbersome. However, experts can use AI to speed up data processing and enable the robot to make real-time decisions.  Predictive Maintenance AI improves robotic reliability by detecting wear and tear and predicting potential failures to prevent unexpected breakdowns. This is important in high-demand environments like the manufacturing industry, where downtime can be costly.  How is AI Used in Robotics? While the discussion above highlights the benefits of AI in robotics, it does not yet clarify how robotic systems use AI algorithms to operate and execute complex tasks. The most common types of AI robots include:  AI-Driven Mobile Robots An AI-based mobile robot (AMR) navigates environments intelligently, using advanced sensors and algorithms to operate efficiently and safely. It can:  See and understand its surroundings using sensors like cameras, LiDAR, and radar, combined with CV algorithms to detect objects, recognize obstacles, and interpret their environment. Process and analyze data in real time to map out their surroundings, predict potential hazards, and adjust to changes as they move. Find the best path and navigate efficiently using AI-driven algorithms to plan routes, avoid obstacles, and move smoothly in dynamic spaces. Interact naturally with humans using AI-powered speech recognition, gesture detection, and other intuitive interfaces to collaborate safely and effectively. Mobile robots in a warehouse AMRs are highly valuable on the factory floor to improve workflow efficiency and productivity.  For example, in warehouse inventory management, an AMR can intelligently navigate through aisles, dynamically adjust its route to avoid obstacles and congestion, and autonomously transport goods.  Articulated Robotic Systems Articulated robotic systems (ARS), or robotic arms, are widely used in industrial settings for tasks like assembly, welding, painting, and material handling. They assist humans with heavy lifting and repetitive work to improve efficiency and safety. Articulated robot  Modern ARS uses AI to process sensor data, enabling real-time perception, decision-making, and precise task execution. AI algorithms help ARS interpret their operating environment, dynamically adjust movements, and optimize performance for specific applications like assembly lines or warehouse automation. Collaborative Robots Collaborative robots, or cobots, work safely alongside humans in shared workspaces. Unlike traditional robots that operate in isolated environments, cobots use AI-powered perception, ML, and real-time decision-making to adapt to dynamic human interactions. AI-driven computer vision helps cobots detect human movements, recognize objects, and adjust their actions accordingly. ML algorithms enable them to improve task execution over time by learning from human inputs and environmental feedback. NLP and gesture recognition allow cobots to understand commands and collaborate more intuitively with human workers. Cobots: Universal Robots (UR)  Universal Robots' UR Series is a good example of a cobot used in manufacturing. These cobots help with tasks like assembly, packaging, and quality inspection. They work alongside factory workers to improve efficiency and human-robot collaboration. AI-Powered Humanoid Robots AI-based humanoid robots replicate the human form, cognitive abilities, and behaviors. They integrate AI to perform completely autonomous tasks or collaborate with humans. These robotic systems combine mechanical structures with AI technologies like CV and NLP to interact with humans and provide assistance. Sophia at UN For example, Sophia is one of the most well-known AI-powered humanoid robots, developed by Hanson Robotics. Sophia engages with humans using advanced AI, facial recognition, and NLP. She can hold conversations, express emotions, and even learn from interactions. Learn about vision-based articulated robots with six degrees of freedom   AI Models Powering Robotics Development AI is transforming the robotics industry, allowing organizations to build large-scale autonomous systems to handle complex tasks more independently and efficiently.  Key advancements driving such transformation include DL models for perception, reinforcement learning (RL) frameworks for adaptability, motion planning for control, and multimodal architectures for processing different types of information.  Let’s discuss these in more detail:  Deep Learning for Perception DL processes images, text, speech, or time-series data from robotic sensors to analyze complex information and identify patterns. DL algorithms, like convolutional neural networks (CNNs), can analyze image and video data to understand its content. In contrast, Transformer and recurrent neural network (RNN) models process sequential data like speech and text.  A sample CNN architecture for image recognition For instance, AI-based CV models play a crucial role in robotic perception, enabling real-time object recognition, tracking, and scene understanding. Some commonly used models include: YOLO (You Only Look Once): A fast object detection model family that enables real-time localization and classification of multiple objects in a scene, making it ideal for robotic navigation and manipulation. SLAM (Simultaneous Localization and Mapping): A framework combining sensor data with AI-driven mapping techniques to help robots navigate unknown environments by building spatial maps while tracking their position. Semantic Segmentation Models: Assign class labels to every image pixel, enabling a robot to understand scene structure for tasks like autonomous driving and warehouse automation. Common examples include DeepLab and U-Net. DeepSort for Object Tracking: A tracking-by-detection model that tracks objects in real time by first detecting them and assigning a unique ID to each object. Reinforcement Learning for Adaptive Behavior RL enables robots to learn through trial and error by interacting with their environment. The robot receives feedback in the form of rewards for successful actions and penalties for undesirable outcomes. Popular RL frameworks used in robotics include:  Deep Q-Network (DQN): DQN uses DL to learn the Q-function. The technique allows agents to store their experiences in batches and use samples to train the neural network. Lifelong Federated Reinforcement Learning (LFRL): This architecture allows robots to continuously learn and adapt by sharing knowledge across a cloud-based system, enhancing navigation and task execution in dynamic environments. Q-learning: A model-free reinforcement learning algorithm that helps agents learn optimal policies through trial and error by updating Q-values based on rewards received from the environment. PPO (Proximal Policy Optimization): A reinforcement learning algorithm that balances exploration and exploitation by optimizing policies using a clipped objective function, ensuring stable and efficient learning. Multi-modal Models Multi-modal models combine data from sensors like cameras, LiDAR, microphones, and tactile sensors to enhance perception and decision-making. Integrating multiple sources of information helps robots develop a more comprehensive understanding of their environment. Examples of multimodal frameworks used in robotics include: Contrastive Language-Image Pretraining (CLIP): Helps robots understand visual and textual data together, enabling tasks like object recognition and natural language interaction. ImageBind: Aligns multiple modalities, including images, text, audio, and depth, allowing robots to perceive and reason about their surroundings holistically. Flamingo: A vision-language model that processes sequences of images and text, improving robotic perception in dynamic environments and enhancing human-robot communication. Challenges of Integrating AI in Robotics Advancements in AI are allowing robots to perceive their surroundings better, make real-time decisions, and interact with humans. However, integrating AI into robotic systems presents several challenges. Let’s briefly discuss each of them. Lack of Domain-specific Data: AI algorithms require a large amount of good quality data for training. However, acquiring domain-specific data is particularly challenging in specialized environments with unique constraints. For instance, data collection for surgical robots requires accessing diverse real-world medical data, which is difficult due to ethical concerns. Processing Diverse Data Formats: A robotic system often depends on various sensors that generate heterogeneous data types such as images, signals, video, audio, text, and other modalities. Combining these sensors' information into a cohesive AI system is complex. It requires advanced sensor fusion and processing techniques for accurate prediction and decision-making.  Data Annotation Complexity: High-quality multimodal datasets require precise labeling across different data types (images, LiDAR, audio). Manual annotation is time-consuming and expensive, while automated methods often struggle with accuracy. Learn how to use Encord Active to enhance data quality using end-to-end data preprocessing techniques.   How Encord Ensures High-Quality Data for Training AI Algorithms for Robotics Applications The discussion above highlights that developing reliable robotic systems requires extensive AI training to ensure optimal performance. However, effective AI training relies on high-quality data tailored to specific robotic applications.  Managing the vast volume and variety of data presents a significant challenge, necessitating the use of end-to-end data curation tools like Encord to streamline data annotation, organization, and quality control for more efficient AI model development for robotics. 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 models by streamlining data annotation, curation, and visualization. Below are some of Encord’s key features that you can use to develop scalable robotic frameworks. Encord Active for data cleaning  Intelligent Data Curation for Enhanced Data Quality The Encord index offers robust AI-assisted features to assess data quality. It uses semi-supervised learning algorithms to 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 robotic systems. 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 models used in 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. Future of Robotics & AI AI and robotics together are driving transformative changes across various industries. Here are some key areas where these technologies are making a significant impact:  Edge and Cloud Computing Edge computing offers real-time data processing within robotic hardware, which is important for low-latency use cases such as autonomous navigation. Cloud computing provides vast data storage and powerful processors to process large amounts of data for AI model training. This allows robots to react quickly to their immediate surroundings and learn from large data sets. Smart Factories  AI-powered robots are transforming factories, which use automation, IoT, and AI-driven decision-making to optimize manufacturing, streamline workflows, and enhance the supply chain.  Unlike traditional factories that rely on fixed processes and human efforts, smart factories use interconnected machines, sensors, and real-time analytics to adapt to production needs dynamically. These systems enable predictive maintenance, optimization, and autonomous quality control. For example, Ocado’s robotic warehouse uses swarm intelligence to coordinate thousands of small robots for high-speed order fulfillment.  Swarm Robotics  Swarm robotics uses a group of robots to solve a complex task collaboratively. AI makes these swarms coordinate their movements, adapt to changing environments, and perform tasks like search and rescue, environmental monitoring, and agricultural automation.  SwarmFarm Robotics spraying pesticides  For example, SwarmFarm Robotics in Australia uses autonomous robots in precision agriculture. These robots work together to monitor crop health, spray pesticides, and plant seeds. Coordinating their actions allows them to cover large areas quickly and adapt to different field conditions. Space and Planetary Exploration  AI-powered robots play a crucial role in space exploration by navigating unknown terrains, conducting scientific experiments, and performing maintenance in harsh environments. AI enables these robots to make autonomous decisions in real time, which reduces their reliance on direct communication with Earth and overcomes delays caused by vast distances. NASA’s Perseverance rover For example, NASA’s Perseverance rover on Mars features AI-driven systems that enable it to navigate the Martian surface autonomously. The rover uses AI to identify and avoid obstacles, choose its paths, and select expected locations for scientific analysis. This autonomy is crucial for exploring areas where real-time communication is not feasible. AI in Robotics: Key Takeaways AI is transforming robotics by enabling machines to perceive, learn, and make intelligent decisions. This transformation is driving advancements across industries, from manufacturing to healthcare. Below are the key takeaways on how AI is shaping robotic automation.  AI Transforms Robotics: AI enhances robotic capabilities by improving decision-making, perception, and adaptability, making robots more autonomous and efficient. Challenges of Incorporating AI in Robotics: Integrating AI in robotics comes with challenges such as acquiring domain-specific data, processing diverse sensor inputs, ensuring AI explainability, achieving scalability across environments, and maintaining seamless hardware integration for optimal performance. Encord for Robotics: Encord provides AI-powered tools for high-quality data annotation and management, enhancing AI model training for robotics. 📘 Download our newest e-book, The rise of intelligent machines to learn more about implementing physical AI models.

Consider a boxy robot nicknamed “Shakey” developed by Stanford Research Institute (SRI) in the 1960s. This robot was named “Shakey” for its trembling movements. It was the first robot that could perceive its surroundings and decide how to act on its own​.  Shakey Robot (Source) It could navigate hallways and figure out how to go around obstacles without human help. This machine was more than a curiosity. It was an early example of giving artificial intelligence a physical body. The development of Shakey marked a turning point as artificial intelligence (AI) was no longer confined to a computer, it was acting in the real world. The concept of Embodied AI began to gain momentum in the 1990s, inspired by Rodney Brooks's 1991 paper, "Intelligence without representation." In this work, Brooks challenged traditional AI approaches by proposing that intelligence can emerge from a robot's direct engagement with its environment, rather than relying on complex internal models. This marked a significant shift from earlier AI paradigms, which predominantly emphasized symbolic reasoning. Over the years, progress in machine learning, particularly in deep learning and reinforcement learning, has enabled robots to learn through trial and error to enhance their capabilities. Today, Embodied AI is evident in a wide range of applications, from industrial automation to self-driving cars, reshaping the way we interact with and perceive technology. Embodied AI is an AI inside a physical form. In simple terms, it is AI built into a tangible system (like a robot or self-driving car) that can sense and interact with its environment​. A modern day example of embodied AI in a humanoid form is Phoenix, a general-purpose humanoid robot developed by Sanctuary AI. Like Shakey, Phoenix is designed to interact with the physical world and make its own decisions. Phoenix benefits from decades of advances in sensors, actuators, and artificial intelligence. Phoenix - Machines that Work and Think Like People (Source) What is Embodied AI? Embodied AI is about creating AI systems that are not just computational but are part of physical robots. These robots can sense, act, and learn from their surroundings, much like humans do through touch, sight, and movement. What is Embodied AI? (Source) The idea comes from the "embodiment hypothesis," introduced by Linda Smith in 2005. This hypothesis says that thinking and learning are influenced by constant interactions between the body and the environment. It connects to earlier ideas from philosopher Maurice Merleau-Ponty, who wrote about how perception is central to understanding and how the body plays a key role in shaping that understanding. In practice, Embodied AI brings together areas like computer vision, environment modeling, and reinforcement learning to build systems that get better at tasks through experience. A good example is robotic vacuum cleaners Roomba. Roomba uses sensors to navigate its physical environment, detect obstacles, and learn the layout of a room and adjust its cleaning strategy based on the data it collects. This allows it to perform actions (cleaning) directly within its surroundings, which is a key characteristic of embodied AI. Roomba Robot (Source) How Physical Embodiment Enhances AI Giving AI a physical body, like a robot, can improve its ability to learn and solve problems. The main benefit is that an embodied AI can learn by trying things out in the real world, not just from preloaded data. For example, think about learning to walk. A computer simulation can try to figure out walking in theory, but a robot with legs will actually wobble, take steps, fall, and try again which enables it to learn a bit more each time. This is just like a child learning to walk by falling and getting back up, the robot improves its balance and movement through real-world experience. Physical feedback, like falling or staying upright, teaches the AI what works and what does not work. This kind of hands-on learning is only possible when the AI has a body to act with. Real-world interaction also makes AI more adaptable. When an AI can sense its surroundings, it isn’t limited to what it was programmed to expect, rather it can handle surprises and adjust. For example, a household robot learning to cook might drop a tomato, feel the mistake through touch sensors, and learn to grip more gently next time. If the kitchen layout changes, the robot can explore and update its understanding. Embodied AI also combines multiple senses, called multimodal learning, to better understand its environment. For example, a robot might use vision to see an object and touch to feel it, creating a richer understanding. A robotic arm assembling something doesn’t just rely on camera images, it also feels the resistance and weight of parts as it works. This combination of senses helps the AI develop an intuitive grasp of physical tasks. Even simple devices, like robotic vacuum cleaners, show the power of embodiment. They learn the layout of a room by bumping into walls and furniture, improving their cleaning path over time. This ability to learn through real-world interaction by using sight, sound, touch, and movement gives embodied AI a practical understanding that software-only AI can not achieve. It is the difference between knowing something in theory and truly understanding it through experience. Applications of Embodied AI Embodied AI has several applications across various industries and domains. Here are a few key applications of Embodied AI. Autonomous Warehouse Robots Warehouse robots are a popular application of embodied AI. These robots transform how goods are stored, sorted, and shipped in modern logistics and supply chain operations. These robots are designed to automate repetitive, time-consuming, and physically demanding tasks to improve efficiency, accuracy, and safety in warehouses. For example, Amazon uses robots (e.g. Digit) in its fulfillment centers to streamline the order-picking and packaging process. These robots are the example of embodied AI because they learn and operate through direct interaction with their physical environment. Embodied AI Robot Digit (Source) Digit relies on sensors, cameras, and actuators to perceive and interact with its surroundings. For example, Digit uses its legs and arms to move and manipulate objects. This physical interaction generates real-time feedback that allow the robots to learn from their actions such as adjusting its grip on an item or navigating around obstacles. The robots improve their performance through repeated practice. For example, Digit learns to walk and balance by experiencing different surfaces and adjusting its movements accordingly.  Inspection Robots  Spot robot from Boston Dynamics is designed for a variety of inspection and service tasks. Spot is a mobile robot and is adaptable to different environments such as office, home,  and outdoors such as construction sites, remote industrial facilities etc. With its four legs, Spot can navigate uneven terrain, stairs, and confined spaces that wheeled robots may struggle with. This makes it ideal for inspection tasks in challenging environments. Spot is equipped with camera, depth sensors, and microphone to gather environmental data. This allows it to perform tasks like detect structural damages, monitor environmental conditions, and even record high-definition video for remote diagnostics. While Spot can be operated remotely, it also has autonomous capabilities. It can patrol pre-defined routes, identify anomalies, and alert human operators in real time. Spot can learn from experience and adjust its behavior based on the environment. Spot Robot (Source) Autonomous Vehicles (Self-Driving Cars) Self-driving cars, developed by companies like Waymo, Tesla, and Cruise, use embodied AI  for decision-making and actuation systems to navigate complex road networks without human intervention. These vehicles use a combination of cameras, radar, and LiDAR to create detailed, real-time maps of their surroundings. AI algorithms process sensor data to detect pedestrians, other vehicles, and obstacles and allow the car to make quick decisions such as braking, accelerating, or changing lanes. Self-driving cars often communicate with cloud-based systems and other vehicles to update maps and learn from shared driving experiences which improve safety and efficiency over time. Vehicles uses Embodied AI from Wayve AI (Source) Service Robots in Hospitality and Retail Embodied AI is transforming the hospitality and retail industries by revolutionizing customer interaction. Robots like Pepper are automating service tasks and enhancing guest experiences. Robots like this serve as both information kiosks and interactive assistants. For example, the Pepper robot uses computer vision and NLP to understand and interact with customers. It can detect faces, interpret gestures, and process spoken language which allow it to provide personalized greetings and answer common questions. Paper is equipped with sensors such as depth cameras and LIDAR to navigate through complex indoor environments. In retail settings, it can lead customers to products or offer store information. In hotels, similar robots might be tasked with delivering room service or even handling luggage by autonomously moving through corridors and elevators. These service robots learn from interactions. For example, it may adjust its speech and gestures based on customer demographics or feedback. Pepper robot from SoftBank (Source) Humanoid Robots Figure 2 is a humanoid robot developed by Figure.ai that gives AI a tangible, interactive presence. Figure 2 integrates advanced sensory inputs, real-time processing, and physical actuation which enable it to interact naturally with its surroundings and humans. Its locomotion capabilities are supported by real-time feedback from sensors, such as cameras and inertial measurement units, enabling it for smooth and adaptive movement across different surfaces and around obstacles. The robot uses integrated computer vision systems to recognize and interpret its surroundings. Figure 2 uses NLP and emotion recognition to engage in conversational interactions. Figure can learn from experience and refine its responses and behavior based on accumulated data from its operating environment which make it efficient to act in a real-world environment to complete designated tasks. Figure 2 Robot (Source) Difference Between Embodied AI and Robotics Robotics is the field of engineering and science focused on designing, building, and operating robots which are physical machines that can perform tasks automatically or with minimal human help. These robots are used in areas like manufacturing, exploration, entertainment etc. The field includes the hardware, control systems, and programming needed to create and run these machines. Embodied AI, on the other hand, refers to AI systems built into physical robots, allowing them to sense, learn from, and interact with their environment through their physical form. Inspired by how humans and animals learn through sensory and physical experiences, Embodied AI focuses on the robot's ability to adapt and improve its behavior using techniques like machine learning and reinforcement learning.   For example, a robotic arm in a car manufacturing plant is programmed to weld specific parts in a fixed sequence. It uses sensors for precision but does not learn or adapt its welding technique over time. This is an example of robotics, relying on traditional control systems without the learning aspect of Embodied AI. On the other hand, ATLAS from Boston Dynamics learns to walk, run, and perform tasks by interacting with its environment and improving its skills through experience. This demonstrates Embodied AI, as the robot's AI system adapts based on physical feedback. Robotics vs Embodied AI (Source: FANUC, Boston Dynamics) Future of Embodied AI The future of Embodied AI depends on advancement of exciting trends and technologies that will make robots smarter and more adaptable. The Embodied AI is set to change both our industries and everyday lives. As Embodied AI relies on machine learning, sensors, and robotics hardware, the stage is set for future growth. Following are key emerging trends and technological advancement that make this happen. Emerging Trends Advanced Machine Learning: Robots will use generative AI and reinforcement learning to master complex tasks quickly and adapt to different situations. For example, a robot could learn to assemble furniture by watching videos and practicing, handling various designs with ease. Soft Robotics: Robots made from flexible materials will improve safety and adaptability, especially in healthcare. Think of a soft robotic arm helping elderly patients, adjusting its grip based on touch. Multi-Agent Systems: Robots will work together in teams, sharing skills and knowledge. For instance, drones could collaborate to survey a forest fire, learning the best routes and coordinating in real-time. Human-Robot Interaction (HRI): Robots will become more intuitive, using natural language and physical cues to interact with people. Service robots, like SoftBank’s Pepper, could evolve to predict and meet customer needs in places like stores Technological Advances Improved Sensors: Improvement in LIDAR, tactile sensors, and computer vision will help robots understand their surroundings more accurately. For example, a robot could notice a spill on the floor and clean it up on its own. Energy-Efficient Hardware: New processors and batteries will make robots last longer and move more freely, which is important for tasks like disaster relief or space missions. Simulation and Digital Twins: Robots will practice tasks in virtual environments before doing them in the real world.  Neuromorphic Computing: Human Brain inspired chips could help robots process sensory data more like humans, making robots like Boston Dynamics’ Atlas even more agile and responsive. Data Requirements for Embodied AI The ability of Embodied AI to learn from and adapt to environments depends on the data on which it is trained. Therefore the data play an important role in building Embodied AI. Following are the data requirements for Embodied AI. Large-Scale, Diverse Datasets Embodied AI systems need a large amount of data about different environments and sources to learn effectively. This diversity helps the AI understand a wide range of real-world scenarios, from different lighting and weather conditions to various obstacles and environments. Real-Time Data Processing and Sensor Integration Embodied AI systems use sensors like cameras, LIDAR, and microphones to see, hear, and feel their surroundings. Processing this data quickly is crucial. Therefore the real-time data processing solution (e.g., GPUs, neuromorphic chips)  is required to allow the AI to make immediate decisions, such as avoiding obstacles or adjusting its actions as the environment changes. Data Labeling Data labeling is a process to give meaning to raw data (e.g., “this is a door,” “this is an obstacle”). It is used to guide supervised learning models to recognize patterns correctly. Poor labeling leads to errors, like a robot misidentifying a pet as trash. Data labeling is a tedious job, data labeling tools with AI assisted labeling is needed for such tasks. Quality Control High-quality data is key to reliable performance. Data quality control means checking that the information used for training is accurate and free from errors. This ensures that the AI learns correctly and can perform well in real-world situations. The success of embodied AI depends on  large and diverse datasets, the ability to process sensor data quickly, clear labeling to teach the model, and rigorous quality control to keep the data reliable.   How Encord Contributes to Building Embodied AI The Encord platform is uniquely suited to support embodied AI development by enabling efficient labeling and management of multimodal dataset that include audio, image, video, text, and document data. This multimodal data is essential for training intelligent systems as Embodied AI relies on such large multimodal datasets.  Encord, a truly multimodal data management platform For example, consider a domestic service robot designed to help manage household tasks. This robot relies on cameras to capture images and video for object and face recognition, microphones to interpret voice commands, and even text and document analysis to read user manuals or labels on products. Encord streamlines the annotation process for all these data types, ensuring that the robot learns accurately from diverse sources. Key features include: Multimodal Data Labeling: Supports annotation of audio, image, video, text, and document data. Efficient Annotation Tools: Encord provides powerful tools to quickly and accurately label large datasets. Robust Quality Control: By offering robust quality control features, Encord ensures that the data used to train embodied AI is reliable and error free. Scalability: Embodied AI systems require large data from various environments and conditions. Encord helps manage and organize these large, diverse datasets to make it easier to train AI that can operate in the real world. Collaborative Workflow: Encord simplifies the collaboration between data scientists and engineers to refine models. These capabilities supported in Encord enable developers to build embodied AI systems that can effectively interpret and interact with the world through multiple sensory inputs. Thus, Encord helps in building smarter, more adaptive Embodied AI applications. Key Takeaways Embodied AI integrates AI into physical machines to enable them to interact, learn, and adapt from real-world experiences. This approach moves beyond traditional, software only AI by providing robots with sensory, motor and learning capabilities. Embodied AI systems can learn from real-world feedback such as falling, balancing, and tactile feedback that is much like humans learn through experience. Embodied AI systems use a combination of vision, sound, and touch to achieve a deeper understanding of their surroundings, which is crucial for adapting to new challenges. Embodied AI is transforming various industries, including logistics, security, autonomous vehicles, and service sectors. The effectiveness of embodied AI depends on large-scale, diverse, and well annotated datasets that capture real-world complexity. Encord platform helps in labelling efficient, multimodal data and quality control. It supports the development of smarter and more adaptable embodied AI systems. 📘 Download our newest e-book, The rise of intelligent machines to learn more about implementing physical AI models.

With the world’s population projected to reach 9.7 billion by 2050, the demand for food is skyrocketing. However, farmers face unprecedented challenges due to labor shortages, climate change, and the need for sustainable practices. This is putting immense pressure on traditional farming methods.  For instance, manual weed control alone can cost farmers billions annually, while inefficient resource use leads to environmental degradation. Enter agricultural drones and robotics, a technological revolution set to transform farming as we know it. Due to their significant benefits, the global agricultural drone market is expected to grow to $8.03 billion by 2029, driven by the urgent need for smarter, more efficient farming solutions. From AI-powered weed targeting to real-time crop health monitoring, these technologies are not just tools. They are the future of agriculture. Yet, despite their potential, adopting these technologies poses a challenge. High upfront costs, technical complexity, and resistance to change often hinder widespread implementation. In this post, we’ll discuss the data and tools required to build these systems, the challenges developers face, and how tools like Encord can help you create scalable robotic systems. What is an Agricultural Drone? An agricultural drone is an unmanned aerial vehicle (UAV) designed to assist farmers by automating crop monitoring, spraying, and mapping tasks. These drones, equipped with advanced sensors, GPS, and AI-powered analytics, capture high-resolution images, analyze soil health, and detect plant stress. Some models even perform precision spraying, reducing chemical usage and improving efficiency. Benefits like automated takeoff and obstacle avoidance enable smooth operations in challenging farming environments. This saves time, lowers labor costs, and enhances yield predictions by providing real-time insights. Drones also allow farmers to perform precision agriculture, which helps them optimize resource use, minimize waste, and increase sustainability. DJI agriculture drone For instance, the DJI Agras T40, a leading spray drone, features advanced payload capabilities for effective crop protection. These machines help automate agricultural workflows and enable farmers to operate them via remote control for timely interventions. How Has the Agricultural Done Industry Transformed in the Past 5 Years? Over the past five years, agricultural drones have evolved from niche tools to essential components of precision farming. These innovations, driven by rapid technological advancements, regulatory support, and growing market demand, transform how farmers monitor crops, apply resources, and automate labor-intensive tasks. Technological Advancements The past five years have witnessed agricultural drones undergo significant technological evolution. Advancements in sensor technology, including multispectral and hyperspectral imaging, have enhanced the ability to monitor crop health with greater precision.  Battery life and propulsion system improvements have extended flight durations, allowing drones to cover larger areas in a single mission. Integration with artificial intelligence (AI) and machine learning (ML) algorithms has enabled real-time data processing.  These trends are leading to immediate decision-making for tasks like variable-rate application of fertilizers and pesticides to improve crop yields. Additionally, the development of autonomous flight functionality has reduced the need for manual intervention, making drone operations more efficient and user-friendly. Regulatory Framework The regulatory landscape for agricultural drones has become more structured and supportive. Many countries have established clear guidelines for their use, addressing issues such as airspace permissions, pilot certifications, and safety standards.  For instance, the Federal Aviation Administration (FAA) in the United States has implemented Part 107 regulations, providing a framework for commercial drone use, including agriculture. These regulations have streamlined the process for farmers and agribusinesses to adopt drone technology, ensuring safe and legal operations.  Collaborations between regulatory bodies and industry stakeholders continue to evolve, aiming to balance innovation with safety and privacy concerns. Market and Industry Growth The agricultural drone market has seen significant growth. Currently, the market is approximately $2.41 billion, with projections estimating a size of $5.08 billion by 2030. This trend means a compound annual growth rate of 16% from 2025 to 2030.  Agricultural drone market This expansion is mostly driven by the need for automated farming operations in the face of labor shortages in the agriculture industry. Farmers are recognizing the return on investment that drones offer through enhanced crop monitoring, efficient resource utilization, and improved yields. Top Companies in the Space Several companies are leading the agricultural drone revolution, developing advanced drone solutions that enhance precision farming. DJI, a dominant force in the drone industry, has introduced cutting-edge models tailored for agriculture. The Mavic 3M, a multispectral imaging drone, enables farmers to monitor crop health accurately. This drone uses Real-Time Kinematics (RTK) technology for centimeter-level positioning. For large-scale operations, DJI Agras T50 and T40 drones offer robust crop spraying and spreading capabilities, allowing for efficient pesticide and fertilizer application. These drones integrate AI-powered route planning and RTK positioning to ensure precise operations and minimize environmental impact.  Beyond DJI, Parrot has developed drones with high-resolution imaging capabilities tailored for agricultural use. For example, the Parrot Bluegrass Fields provides in-depth crop analysis and covers up to 30 hectares with a 25-minute flight time.  AgEagle Aerial Systems, known for its eBee Ag unmanned aerial system (UAS), offers aerial mapping solutions to help farmers make data-driven decisions. Meanwhile, XAG, a rising competitor, specializes in autonomous agricultural drones. One example is the XAG P100, which integrates AI and RTK technology for precise spraying and seeding. Such companies are shaping the future of smart agriculture by combining automation, high-resolution imaging, and advanced navigation. Case Study from John Deer John Deere has been at the forefront of integrating autonomous technology into agriculture. In 2022, the company introduced its first autonomous tractor, which has since been used by farmers across the United States for soil preparation.  Building on this success, John Deere plans to launch a fully autonomous corn and soybean farming system by 2030. The system will address labor shortages and enhance productivity.  The company's latest Autonomy 2.0 system features 16 cameras providing a 360-degree view and operates at flight speeds up to 12 mph, a 40% increase over previous models. John Deere seeks to improve agriculture efficiency, safety, and sustainability by automating repetitive tasks. Autonomous Agriculture Beyond Traditional Drones Agricultural drones have transformed how we monitor crops and spray, but the next evolution lies in autonomous agriculture robotics. These systems go beyond aerial capabilities, incorporating ground-based robots that carry out tasks such as planting, weeding, and harvesting with unmatched precision.  The transition from drones to robotics represents a natural progression in precision agriculture. Drones are excellent for aerial data collection and spraying, but ground-based robots can manage more complex, labor-intensive tasks.  For example, robots with computer vision and AI can identify and remove weeds without damaging crops, reducing herbicide use by up to 90%. Robots like FarmWise’s Titan FT-35 use AI to distinguish crops from weeds and mechanically remove invasive plants. Laser-based systems, such as Carbon Robotics’ LaserWeeder, eliminate weeds accurately, saving farmers thousands in herbicide costs.  Additionally, Ground robots with multispectral cameras and sensors can monitor soil moisture, nutrient levels, and plant health in real time. Robots like Ecorobotix’s ARA analyze soil composition and apply fertilizers with variable-rate precision, ensuring optimal nutrient delivery. https://encord.com/blog/computer-vision-in-agriculture/ Data and Tooling Requirements for Building Agricultural Robots Developing agricultural robots requires a comprehensive approach to data and technology. The process begins with collecting high-quality, relevant data, which forms the foundation for training and refining the AI models that enable autonomous operation in agricultural fields. Data Collection Data collection is the most critical aspect of developing agricultural robots. The data must come from various sources to capture the complexity of agricultural environments. This includes real-time data from sensors embedded in robots or placed across fields to measure soil moisture, temperature, pH levels, and nutrient content.  Cameras and multispectral sensors capture detailed imagery of crops, allowing for analysis of plant health, growth stages, and pest presence. Historical data, including weather patterns, previous crop yields, and soil health data, adds layers of predictive capability to AI models. AI and ML Platforms The "brains" of agricultural robots consist of AI and ML algorithms, which require powerful software tools and platforms. These platforms help create and train intelligent models that enable robots to perceive, understand, and act in agricultural environments. Machine Learning and Computer Vision Frameworks ML platforms like TensorFlow and PyTorch train AI models that allow image recognition for weeding and disease detection. Additionally, specialized frameworks from NVIDIA for GPU acceleration enhance speed. OpenCV, an open-source CV library, offers a collection of algorithms for image processing, feature extraction, object detection, video analysis, and more. It is widely used in robotics and provides essential building blocks for vision-based agricultural robot applications. Robotics Middleware and Frameworks ROS (Robot Operating System) is a widely adopted open-source framework for robotics software development. It simplifies sensor integration, navigation, motion planning, and simulation. Key features include: Sensor integration and data abstraction: Provides a unified interface for accessing and processing sensor data. Navigation and localization: Offers pre-built algorithms, mapping tools, and localization techniques (e.g., SLAM) for autonomous robot navigation. Simulation Environments: ROS integrates seamlessly with simulation environments like Gazebo. It enables developers to test and validate robot software in a virtual world before deploying it to real hardware. Edge AI Platforms NVIDIA Jetson embedded computing platforms (e.g., Jetson AGX Orin, Jetson Xavier NX) are widely used in robotics to balance performance and energy efficiency. They provide potent GPUs and execute complex AI models directly on robots in real-time. Google Coral provides edge TPU (Tensor Processing Unit) accelerators that are specifically designed for efficient inference of TensorFlow Lite models. Coral platforms are cost-effective and energy-efficient. This makes them suitable for deploying lightweight AI models on robots operating in power-constrained environments. Hardware Considerations and Software Integration Requirements Selecting the appropriate hardware is equally important because the physical environment of a farm is harsh and unpredictable. Robots must be designed to withstand dust, water, extreme temperatures, and physical shocks.  This requires selecting durable materials for the robot's body, ensuring that sensors and cameras are both protected and functional. It is also important to choose batteries that provide long life and fast recharge capabilities. The software must also be robust and capable of managing diverse data inputs, processing them efficiently, and sending commands to the robotic systems. Additionally, the software should integrate seamlessly with existing farm management software, Internet-of-Things (IoT) devices, and other agricultural robots or drones for an effective farm management solution.  Challenges of Building Agricultural Robots Despite the advantages of deploying agricultural robots, several challenges stand in the way of their widespread adoption and effective operation. Environmental factors: Agricultural robots face challenges due to unpredictable environments, including rough terrain, mud, and severe weather, which can affect their sensors and mobility systems. Hyperspectral cameras and LiDAR often fail in fog or low-light conditions, reducing data accuracy. Regulatory constraints: Varied regulations across regions can limit operational areas and require certifications. Additionally, they impose data privacy and usage restrictions, complicating operations. High initial costs: Significant upfront costs are associated with research, engineering, and software development. High-performance components contribute to expensive robot systems. Collecting and labeling large datasets for AI training is resource-intensive. Data quality: Robots rely on high-quality data for disease detection and yield prediction tasks. However, bias in training data poses challenges, such as models trained on monoculture farms failing in diverse cropping systems. Additionally, annotating crop imagery for ML requires precise tagging of subtle features, which is time-intensive and error-prone. Maintenance: Regular maintenance is necessary in harsh agriculture, but it can be logistically challenging and costly, particularly in remote or expansive farming areas. How Encord Helps Build Agricultural Drones: Conquering Data Challenges With a Data-Centric Platform As we discussed, building efficient agricultural robots presents numerous challenges, mainly due to the inherent complexities of agricultural data. Agricultural sensor data is often noisy and imperfect due to environmental factors. Human annotation can introduce errors and inconsistencies, which can impact model accuracy.  These data quality challenges can greatly hinder developing and deploying effective agricultural drone and robot systems. Recognizing that quality data is not just a component but the cornerstone of successful AI, platforms like Encord are specifically designed to address these critical data challenges.   Encord provides a comprehensive, data-centric environment tailored to streamline the AI development lifecycle for CV applications in demanding fields like agricultural drones and robotics. It also enables effective management and curation of large datasets while facilitating the iterative improvement of model performance through intelligent, data-driven strategies. Below are some of its key features that you can use for agricultural drone development. Key Takeaways Agricultural drones are transforming farming by enabling precision agriculture, reducing labor costs, and optimizing resource use. With advancements in AI and automation, these drones are becoming more efficient and accessible. Governments are supporting adoption through regulations, and the market is expected to grow significantly. Beyond drones, ground-based robotics are shaping the future of fully autonomous farming, driven by data and AI-powered analytics. 📘 Download our newest e-book, The rise of intelligent machines to learn more about implementing physical AI models.

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.