aiqbench analysis
The core contribution is a learned controller: a muscle-driven imitation policy generates knee moments in simulation, and a distilled estimator running on onboard sensors transfers to hardware without device-specific data. The reported 21.5% reduction in peak knee moment and 26.7% reduction in vastus medialis activation come from seven participants, so the evidence is preliminary. For ML readers, the useful signal is the sim-to-real transfer pattern and the open release of hardware and pipeline, which would allow controllers to be compared on shared hardware.
Design and Validation of a Robotic Knee Exoskeleton with Musculoskeletal Simulation-Derived Biological Torque Control
arXiv:2610.41981v1 Announce Type: new Abstract: Data-driven controllers have enabled lower-limb robotic exoskeletons to adapt assistance to the user across many tasks. A leading example is biological torque control, which estimates the user's own joint moment from onboard sensors and assists in proportion to that estimate. However, its estimators are trained on data recorded with the target device, so deploying the controller on new hardware requires collecting new data. Existing open-source efforts release hardware, software, or controllers separately, so this data collection must still be repeated on the assembled device. Here, we present an open-source knee exoskeleton, built from off-the-shelf components and widely available fabrication tools, with a biological torque controller derived entirely from simulation. A muscle-driven imitation policy reproduces squatting motions from an open biomechanics dataset, and the resulting knee moments are distilled into an estimator that uses only onboard sensors, so no training data are recorded on the device. In seven able-bodied participants, assistance reduced peak biological knee extension moment by 21.5% and peak vastus medialis activation by 26.7% during squatting, relative to squatting without the exoskeleton (p<0.05). Our results demonstrate that a biological torque controller derived from simulation can transfer to physical hardware without device-specific data collection. Because the hardware and training pipeline are released together, research groups can train and compare knee exoskeleton controllers on shared hardware without additional data collection overhead.
Abstract:Data-driven controllers have enabled lower-limb robotic exoskeletons to adapt assistance to the user across many tasks. A leading example is biological torque control, which estimates the user's own joint moment from onboard sensors and assists in proportion to that estimate. However, its estimators are trained on data recorded with the target device, so deploying the controller on new hardware requires collecting new data. Existing open-source efforts release hardware, software, or controllers separately, so this data collection must still be repeated on the assembled device. Here, we present an open-source knee exoskeleton, built from off-the-shelf components and widely available fabrication tools, with a biological torque controller derived entirely from simulation. A muscle-driven imitation policy reproduces squatting motions from an open biomechanics dataset, and the resulting knee moments are distilled into an estimator that uses only onboard sensors, so no training data are recorded on the device. In seven able-bodied participants, assistance reduced peak biological knee extension moment by 21.5% and peak vastus medialis activation by 26.7% during squatting, relative to squatting without the exoskeleton (p<0.05). Our results demonstrate that a biological torque controller derived from simulation can transfer to physical hardware without device-specific data collection. Because the hardware and training pipeline are released together, research groups can train and compare knee exoskeleton controllers on shared hardware without additional data collection overhead.
Robotics (cs.RO)
arXiv:2610.41981 [cs.RO]
(or arXiv:2610.41981v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.41981 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 313 words