Robot predicts human moves for smoother team object carrying
Learning from Humans for Proactive Assistance in Human-Robot Collaborative Transport
RoboticsHuman-Computer InteractionMachine Learning
Summary
Moving big or heavy things with a robot can be hard because the robot needs to both help carry and react to the person’s actions. The authors created a system called PROACT that lets a robot guess what the human will do next and work along with them smoothly. This system was trained using a lot of examples of people working together to carry objects. In tests, PROACT helped the robot and human complete their tasks faster and with less effort than other methods.
What this means in practice
- •For logistics operators: Integrate PROACT to improve teamwork between robots and workers when moving bulky items through complex environments.
- •For warehouse automation teams: Use PROACT to develop robots that anticipate human input for more efficient and responsive co-transport of heavy products.
Authors
Elvin Yang, Christoforos Mavrogiannis
Abstract
We focus on human-robot collaborative transport, a challenging task of broad relevance spanning logistics, manufacturing, and the home, in which a user and a robot work together to relocate a large or heavy object. To act as an effective partner, the robot should reduce the user's effort by contributing to efficient relocation of the object while remaining physically responsive to them. Prior work often addresses these capabilities separately, producing robots that may move the object efficiently but resist user input, or accommodate the user but depend on continuous guidance. Our key insight is that obstacle-constrained collaborative transport requires integrating predictions of human collaborative behavior with compliant robot control. To this end, we introduce PROACT, a framework for human-robot collaborative transport that incorporates anticipation into compliant whole-body control through a learned model of human collaborative behavior. Trained on a large-scale, real-world dataset of dyadic human transport demonstrations, our transformer architecture distills collaborative behavior into predictions of future object motion. Across 108 real-world trials with a 9-DoF mobile manipulator, PROACT reduces mean interaction work by 59.2\% and 20.4\%, and mean completion time by 12.9\% and 6.9\%, relative to compliance-only and MPC baselines, respectively. Footage from our experiments can be found at https://youtu.be/qAGvQfVPjbk.