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arXiv Machine Learning

Reflex-Informed Neuromuscular Reinforcement Learning for Muscle-Driven Locomotion

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.

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arXiv Machine Learning
Aug 19

Lambda-Hold Control: Human-Like Movement Emerges from a Minimal Task Reward in Predictive Musculoskeletal Simulation

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.

By Jun Hyuk Lee, Chihyeong Lee, Jooeun Ahn
reinforcement-learning
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arXiv Machine Learning
Sep 21

Beyond Kinematics: Benchmarking Simulation Fidelity for Muscle-Driven Imitation Learning

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.

By Ayah G. Ahmad, Claire E. Borden, Maegan Tucker
reinforcement-learningroboticsbenchmarks
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arXiv Machine Learning
Sep 18

UniExo: Unified Multi-Skill Policies for Musculoskeletal Locomotion and Co-Adaptive Exoskeleton Control

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.

By Yifei Yuan, Jakob Wolf, Ghaith Androwis, Xianlian Zhou
agentsreinforcement-learningroboticsfine-tuning
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arXiv Machine Learning
Aug 6

Imitation Learning from Human Motion Alone Does Not Guarantee Biomechanically Plausible Gait Kinetics

arXiv:2603. 12408v3 Announce Type: replace-cross Abstract: Motion imitation learning (IL) is increasingly used in robotics and human gait modeling, yet its ability to recover biomechanically consistent joint moments without explicit kinetic information remains unclear.

By Xinyi Liu, Jangwhan Ahn, Edgar Lobaton, Jennie Si, He Huang
roboticsbenchmarks
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arXiv AI
Aug 5

MIMIC-MJX: Neuromechanical Emulation of Animal Behavior

arXiv:2511. 20532v3 Announce Type: replace-cross Abstract: The primary output of the nervous system is movement and behavior.

By Charles Y. Zhang (Harvard University), Yuanjia Yang (Salk Institute for Biological Studies), Aidan Sirbu (Mila), Elliott T. T. Abe (University of Washington), Emil W\"arnberg (Harvard University), Eric J. Leonardis (Salk Institute for Biological Studies), Diego E. Aldarondo (Harvard University), Adam Lee (Harvard University), Aaditya Prasad (Massachusetts Institute of Technology), Jason Foat (Salk Institute for Biological Studies), Kaiwen Bian (Salk Institute for Biological Studies), Joshua Park (Salk Institute for Biological Studies), Rusham Bhatt (Salk Institute for Biological Studies), Vyom N. Patel (Neuromatch), Hutton Saunders (Salk Institute for Biological Studies), Austin O. Barbano (Salk Institute for Biological Studies), Akira Nagamori (Salk Institute for Biological Studies), Ayesha R. Thanawalla (Salk Institute for Biological Studies), Kee Wui Huang (Salk Institute for Biological Studies), Fabian Plum (Imperial College London), Hendrik K. Beck (Imperial College London), Steven W. Flavell (Massachusetts Institute of Technology), David Labonte (Imperial College London), Blake A. Richards (Mila), Bingni W. Brunton (University of Washington), Eiman Azim (Salk Institute for Biological Studies), Bence P. \"Olveczky (Harvard University), Talmo D. Pereira (Salk Institute for Biological Studies)
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arXiv Machine Learning
Aug 4

Staged Multi-Agent Training (SMAT) for Hip Exoskeletons: Metabolic and Biomechanical Validation of a Simulation-Trained Co-Adaptive Controller

arXiv:2608. 00715v1 Announce Type: cross Abstract: Learning-based controllers can deliver exoskeleton assistance after training entirely in physics-based simulation, yet few controllers that address human-device co-adaptation have been validated on real users by whole-body metabolic measurement, the standard benchmark for assistive walking.

By Yifei Yuan, Jakob Wolf, Ghaith Androwis, Xianlian Zhou
agentsbenchmarks
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