Proactive Ergonomic Human Motion Generation for Human-Humanoid Collaboration

초록

This paper presents a human-centric proactive ergonomic safety framework for humanhumanoid collaboration, aiming to ensure that human motion remains physically safe and ergonomically compliant throughout cooperative tasks. A vector auto-regressive model predicts short-term human joint trajectories based on repetitive task data, and the predicted motion is tracked by a virtual human model. Control Barrier Functions enforce ergonomic constraints in real time, including stance stability via the Center of Pressure and effort alignment via the force manipulability ellipsoid. Simulations of collaborative lifting tasks demonstrate that the framework effectively constrains predicted motions within an ergonomic safety set. Quantitative results show that average overloading joint torque was reduced by 24.6 % at the shoulder, and total biomechanical effort decreased by 48.1 % and 60.8 % at the shoulder and elbow, respectively. These reductions highlight the potential of our framework to enable humanoid systems that proactively adapt to human motion while maintaining ergonomic safety in physically collaborative tasks.

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