Context-Continuous Preference Learning for Exoskeleton Personalization
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2609.14765v1 Announce Type: cross Abstract: Hip exoskeletons may improve recovery from unexpected gait perturbations, yet personalizing assistance remains difficult because balance is multidime...
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.
arXiv:2608. 15995v1 Announce Type: cross Abstract: Upper extremity motor function recovery is positively linked to Task-Specific Training (TST) and sufficient therapy dosage.
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.
arXiv:2607. 23304v1 Announce Type: cross Abstract: Modern predictive systems are expected to adapt their behavior to the specific situation they are facing.
arXiv:2607. 28854v1 Announce Type: new Abstract: Discrete choice modeling is a common tool used for preference elicitation during policy-making, but this is typically done through parametric models.