arXiv:2508. 18066v2 Announce Type: replace-cross Abstract: Controlling high-dimensional and nonlinear musculoskeletal models of the human body is a foundational scientific challenge.
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UniExo is a framework that builds a single, multi-skill musculoskeletal human policy by distilling four imitation experts—walking, turning, running, and backward walking—into one network guided by a skill latent. The human policy is fine‑tuned with reinforcement learning on transition sequences, achieving a 94.7% tracking success rate on unseen clips and greater robustness to perturbations. A hip exoskeleton controller is then co‑adapted with this human policy via multi‑agent reinforcement learning, enabling it to assist across four treadmill speeds and a continuous route of all four skills without explicit mode switching.
The paper compares two leading motion‑imitation reinforcement learning pipelines—HyFyDy, which uses detailed musculotendon modeling, and MuJoCo, which focuses on computational speed. Using the same human motion‑capture and EMG data, both pipelines reproduce kinematics similarly, but HyFyDy’s muscle activation predictions align more closely with experimental EMG (RMSE 0.164, r = 0.4) than MuJoCo’s (RMSE 0.344, r = 0.11). The authors conclude that HyFyDy’s higher physiological realism makes it currently more suitable for musculoskeletal modeling, though both systems need further development for GPU‑parallelizable environments and robotic assistive‑device design.
arXiv:2609.16697v1 Announce Type: cross Abstract: World models connect perception and decision-making in embodied intelligence by maintaining hidden state, anticipating consequences, comparing interv...
arXiv:2604. 08780v2 Announce Type: replace-cross Abstract: World models promise a paradigm shift in robotics, where an agent learns the physics of its environment once and then acquires behaviors efficiently.
The paper introduces the λ-hold controller, a minimal-task-reward approach inspired by the equilibrium-point hypothesis, to train a muscle-actuated skeletal model for human-like sprinting. By fixing each muscle’s EP threshold length λ over gait-phase intervals, the controller dramatically reduces the action space and the frequency of policy queries, enabling efficient exploration and learning within an hour of training. This method demonstrates that physiologically grounded control can produce realistic human motion in predictive musculoskeletal simulations.
The paper introduces a Reflex-Informed Neuromuscular Reinforcement Learning framework for muscle-driven locomotion. It combines a fixed phase-dependent reflex controller with a reinforcement learning policy that adjusts reflex gains and thresholds for hip swing, knee support, and ankle propulsion. Experiments show the method produces physiologically plausible walking with improved kinematic accuracy, dynamic consistency, symmetry, and stride consistency, and remains robust to muscle weakness and perturbations without retraining.