Biomechanics-Aware Trajectory Optimization for Online Navigation during Robotic Physiotherapy
Abstract
Robotic devices provide a great opportunity to assist in delivering physical therapy and rehabilitation movements, yet current robot-assisted methods struggle to incorporate biomechanical metrics essential for safe and effective therapy. We introduce BATON, a Biomechanics-Aware Trajectory Optimization approach to online robotic Navigation of human musculoskeletal loads for rotator cuff rehabilitation. BATON embeds a high-fidelity OpenSim model of the human shoulder into an optimal control framework, generating strain-minimizing trajectories for real-time control of therapeutic movements. Its core strength lies in the ability to adapt biomechanics-informed trajectories online to unpredictable volitional human actions or reflexive reactions during physical human-robot interaction based on robot-sensed motion and forces. BATON's adaptability is enabled by a real-time, model-based estimator that infers changes in muscle activity via a rapid redundancy solver driven by robot pose and force/torque sensor data. We validated BATON through physical human-robot interaction experiments, assessing response speed, motion smoothness, and interaction forces.
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