Ground Reaction Force-Aware Model Predictive Control for Underactuated Upright Physical Assistance
Overview
This research introduces Ground Reaction Force-Aware Model Predictive Control (GRF-MPC), a framework that integrates live ground reaction force and center of pressure measurements into a human dynamics model to optimize assistive forces. Deployed on the Robotic Upright Stand Trainer (RobUST) with an underactuated 4-cable configuration, the system was evaluated against an 8-cable baseline across nearly 300 trials with six participants. Results show GRF-MPC reduced peak center-of-mass error by 19.6% during perturbations, lowered trajectory tracking error by 24.1%, decreased net mechanical work by 28.7%, and increased voluntary excursion area by 45.9%. The underlying quadratic program solves in under 1 ms, enabling 100 Hz replanning.
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Reported quantity · number of trials: 300 other · reported trials
“irectly into a human centroidal dynamics model, GRF-MPC formulates a real-time quadratic program (QP) that optimizes assistive forces and moments over a receding horizon. We deployed GRF-MPC on the Robotic Upright Stand Trainer (RobUST) in an underactuated 4-cable configuration and evaluated it against a fully actuated 8-cable Cartesian impedance baseline in nearly 300 trials across six healthy participants. Across t”
Exact source · revision 1Source owner not reported
Reported quantity · number of participants: 6 other · reported trials
“irectly into a human centroidal dynamics model, GRF-MPC formulates a real-time quadratic program (QP) that optimizes assistive forces and moments over a receding horizon. We deployed GRF-MPC on the Robotic Upright Stand Trainer (RobUST) in an underactuated 4-cable configuration and evaluated it against a fully actuated 8-cable Cartesian impedance baseline in nearly 300 trials across six healthy participants. Across t”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalGround Reaction Force-Aware Model Predictive Control for Underactuated Upright Physical Assistance
This research introduces Ground Reaction Force-Aware Model Predictive Control (GRF-MPC), a framework that integrates live ground reaction force and center of pressure measurements into a human dynamics model to optimize assistive forces. Deployed on the Robotic Upright Stand Trainer (RobUST) with an underactuated 4-cable configuration, the system was evaluated against an 8-cable baseline across nearly 300 trials with six participants. Results show GRF-MPC reduced peak center-of-mass error by 19.6% during perturbations, lowered trajectory tracking error by 24.1%, decreased net mechanical work by 28.7%, and increased voluntary excursion area by 45.9%. The underlying quadratic program solves in under 1 ms, enabling 100 Hz replanning.
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