Collaborative Behavior Models for Optimized Human-Robot Teamwork
This addresses the challenge of intuitive teamwork in robotics for applications like manufacturing or healthcare, though it is incremental as it builds on existing MPC methods.
The paper tackles the problem of enabling fluid human-robot collaboration by developing a Model Predictive Control framework that simultaneously plans robot trajectories and predicts human actions, achieving a high success rate in handover tasks even in cluttered environments with sensor noise.
Effective human-robot collaboration requires informed anticipation. The robot must anticipate the human's actions, but also react quickly and intuitively when its predictions are wrong. The robot must plan its actions to account for the human's own plan, with the knowledge that the human's behavior will change based on what the robot actually does. This cyclical game of predicting a human's future actions and generating a corresponding motion plan is extremely difficult to model using standard techniques. In this work, we describe a novel Model Predictive Control (MPC)-based framework for finding optimal trajectories in a collaborative, multi-agent setting, in which we simultaneously plan for the robot while predicting the actions of its external collaborators. We use human-robot handovers to demonstrate that with a strong model of the collaborator, our framework produces fluid, reactive human-robot interactions in novel, cluttered environments. Our method efficiently generates coordinated trajectories, and achieves a high success rate in handover, even in the presence of significant sensor noise.