Ground Reaction Force-Aware Model Predictive Control for Underactuated Upright Physical Assistance
Ground 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.
The paper reports experimental results on a specific hardware platform (RobUST) using ground reaction force measurements to improve control performance in physical human-robot interaction tasks.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
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number of trials
300 trials
Reported trials
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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
Open source S3number of participants
6
Reported trials
View original evidence
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
Open source S3
- paper: not_reported
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Automatically extracted; no manual editorial approval recorded.
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
Open source S3
Source：arXiv Robotics — research abstracts · arxiv.org