arXiv Machine Learning
Jun 5

OLIVE: Online Low-Rank Incremental Learning for Efficient Adaptive Exoskeletons

arXiv:2606. 05234v1 Announce Type: cross Abstract: Wearable exoskeleton systems hold promise for restoring mobility in individuals with physical impairments, yet most existing controllers rely on static gait policies that lack the ability to adapt to dynamic real-world environments or individual user characteristics.

By Dong Liu, Yanxuan Yu, Ben Lengerich, Tony Geng, Ying Nian Wu
arXiv Machine Learning
Aug 18

Learning Varying Physical Therapist-Patient Interactions for Robot-mediated Upper Limb Task-Specific Training

arXiv:2608. 15995v1 Announce Type: cross Abstract: Upper extremity motor function recovery is positively linked to Task-Specific Training (TST) and sufficient therapy dosage.

By Jia Quan Loh (Human Robotics Laboratory, Department of Mechanical Engineering, The University of Melbourne), Vincent Crocher (Human Robotics Laboratory, Department of Mechanical Engineering, The University of Melbourne), Marlena Klaic (Melbourne School of Health Sciences, The University of Melbourne), Denny Oetomo (Human Robotics Laboratory, Department of Mechanical Engineering, The University of Melbourne), Ying Tan (Human Robotics Laboratory, Department of Mechanical Engineering, The University of Melbourne)
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