Muscle-Aware Retargeting and Null-Space Exploration for Whole-Body Musculoskeletal Control
Overview
This paper introduces a pipeline that transforms monocular video into a trained whole-body imitation policy controlling 700 muscles. The method addresses kinematic plausibility and high-dimensional redundancy through muscle-aware retargeting with hard constraints and null-space exploration for anatomically structured perturbations. On a 24-clip motion-capture benchmark, the approach solves every reference trajectory, reaches the full horizon with half the samples of a baseline pipeline, and reduces joint-center error by 44% without late collapse.
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Reported quantity · muscles: 700 other · Basis not reported
“arXiv:2611.10682v1 Announce Type: new Abstract: Learning whole-body musculoskeletal control from human motion is important for developing human-like robots, assistive devices, and rehabilitation systems. This task presents two main challenges, namely reference trajectories that are kinematically plausible but difficult to reproduce through muscle-driven dynamics, and a highly redundant 700-dimensional action space in”
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Reported quantity · clips: 24 other · reported trials
“s affect muscle-driven tracking. Second, null-space exploration (NSE) estimates a one-control-frame mapping from muscle excitations to generalized forces and injects bounded, anatomically structured perturbations along its null space into the actions of a value-guided flow policy. On a 24-clip motion-capture benchmark, MAR and NSE together solve every reference, reach the full horizon with half the samples of the bas”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalMuscle-Aware Retargeting and Null-Space Exploration for Whole-Body Musculoskeletal Control
This paper introduces a pipeline that transforms monocular video into a trained whole-body imitation policy controlling 700 muscles. The method addresses kinematic plausibility and high-dimensional redundancy through muscle-aware retargeting with hard constraints and null-space exploration for anatomically structured perturbations. On a 24-clip motion-capture benchmark, the approach solves every reference trajectory, reaches the full horizon with half the samples of a baseline pipeline, and reduces joint-center error by 44% without late collapse.
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