Director, Robotics & Autonomous Systems Program at ASTM International
Robot models, especially those trained with lots of data, have recently shown impressive skills in handling objects and moving around in real-world environments. Some studies have shown that with enough training data from a specific environment, robots can adapt their actions to different situations within that space. However, these robots often need extra fine-tuning when introduced to new environments, unlike language or vision models that can work immediately in new settings without additional adjustments. This new research conducted by New York University, Hello Robot Inc, and Meta introduces Robot Utility Models (RUMs), a new way to train and deploy robots that can adapt to new environments without any extra training. To build RUMs, the team developed tools to quickly gather data for tasks involving object manipulation, like opening drawers or picking up items. They used this data to train robots using multi-modal imitation learning and tested the system on a basic robot model called Hello Robot Stretch. The robots achieved an average success rate of 90% in new environments with new objects. The robots were trained for specific tasks such as opening cabinets, picking up napkins, and repositioning fallen items. These utility models also worked well with different robots and camera setups without needing more data or adjustments. Key lessons learned include the importance of quality training data, the need for diverse demonstrations, and the value of strategies that allow robots to retry tasks to improve their performance. 📝 Research Paper: https://lnkd.in/dHN3CctB 📊 Project Page: https://lnkd.in/dq3KC5nU #robotics #reseach