How Pickle Robot Is Using Encord To Build  Physical AI For Warehouse Automation

Introducing: Pickle Robot

Pickle Robot, a Cambridge, MA-based Physical AI innovator, is revolutionizing the logistics industry with cutting-edge applications of hardware, AI, and data. Focused first on optimizing truck unloading and loading, Pickle Robot streamlines one of the most physically demanding tasks in warehousing. 

Historically, the process of unloading non-palletized goods from trailers relied heavily on manual labor, resulting in fatigue and inefficiencies and prone to high worker safety risks.

To address these challenges, a team of MIT graduates came together to form Pickle Robot. They developed a sophisticated solution that uses advanced AI algorithms trained on highly accurate images and videos to automate the unloading of up to 1,500 packages per hour with its green mobile manipulation robots.

The purpose-built solution combines custom AI/ML, highly contextualized data, and off-the-shelf hardware and sensors, significantly reducing the time, effort, and risk in an often overlooked part of the supply chain that warehouses spend over $100 Billion a year to do. Today, they are helping their customers like UPS, Ryobi Tools, and Randa Apparel & Accessories unload millions of pounds of packages monthly.

The data challenge

To build fast and precise robotic systems, Pickle Robot needed to think about the end-to-end experience while facing hurdles across hardware and AI models. Specifically on the model front, Pickle Robot recognized the need for a robust AI system capable of handling diverse cargo. This led the team to implement a unique combination of sensors and machine learning models to identify different types of packages and manipulate various goods accurately. The integration of these technologies enhanced operational efficiency and  minimized errors and downtime. Achieving success required a robust data engine and rich images with precise annotations. 

Prior to Encord, Pickle Robot used data annotation services from other providers. It relied on an outsourced labeling team to conduct the labeling within the software's limitations and the skills of the outsourced labelers. Challenges included:

• Poor labels—overlapping polygons, or, more often than not, a significant number of packages were submitted with incomplete labels.
• Excessive auditing cycles—the legacy approach was error-prone. The lean team of AI and ML engineers spent up to 20+ minutes on auditing tasks per cycle, with high rejection rates. 
• Complex semantic segmentation ontologies were infeasible, which inhibited the robot's ability to accurately understand its operating environment.
• Platform unreliability limited the efficacy of automated workflows and reduced the time available for model development.

Accuracy in training data is critical when your business depends on the accuracy and efficiency of the robotics system's performance. 

Utilising Encord for consolidated data curation & annotation

To address these challenges, Pickle Robot made a strategic decision to partner with Encord.  

With Encord, Pickle Robot gained a platform that does data curation, annotation, and provides robust analytics and model evaluation functionality, with full integration to Pickle Robot's Google Cloud Platform based data engine infrastructure.

The Encord platform enables data management and discoverability capabilities, granular annotation features (bounding boxes, polylines, key points, bitmasks, and polygons), nested ontologies, collaborative workflows, AI-assisted labeling with HITL, and comprehensive data curation functionality required to run efficient data pipelines.

Benefits: Pickle Robot increased precision by 15% and iterating models 60% faster

Since Pickle Robot partnered with Encord, Pickle Robot has seen a drastic improvement in the AI and ML engineers’ productivity, improved precision in task execution, and faster time-to-model improvement.

Key benefits:

• Achieving reliable data pipelines for model training and evaluation 60% faster
• 30% improvement in annotation accuracy
• Faster and more comprehensive audit and review cycles
• Increased observability of real-time data distributions, allowing for rapid domain drift corrections.
• 15% Improvement in robotic grasping accuracy with better training data