SMAT: Staged Multi-Agent Training for Co-Adaptive Exoskeleton Control
Yifei Yuan, Ghaith Androwis, and Xianlian Zhou

TL;DR
This paper introduces SMAT, a staged multi-agent training approach that mimics human adaptation to exoskeletons, resulting in more stable and effective assistance policies validated through simulation and real-world experiments.
Contribution
The paper presents a novel four-stage curriculum for co-adaptive exoskeleton control training, explicitly modeling human motor adaptation to improve assistance effectiveness.
Findings
Achieved 10.1% reduction in hip muscle activation in simulation.
Demonstrated consistent assistance in physical experiments across subjects.
Produced positive mechanical power without subject-specific retraining.
Abstract
Effective exoskeleton assistance requires co-adaptation: as the device alters joint dynamics, the user reorganizes neuromuscular coordination, creating a non-stationary learning problem. Most learning-based approaches do not explicitly account for the sequential nature of human motor adaptation, leading to training instability and poorly timed assistance. We propose Staged Multi-Agent Training (SMAT), a four-stage curriculum designed to mirror how users naturally acclimate to a wearable device. In SMAT, a musculoskeletal human actor and a bilateral hip exoskeleton actor are trained progressively: the human first learns unassisted gait, then adapts to the added device mass; the exoskeleton subsequently learns a positive assistance pattern against a stabilized human policy, and finally both agents co-adapt with full torque capacity and bidirectional feedback. We implement SMAT in the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsProsthetics and Rehabilitation Robotics · Stroke Rehabilitation and Recovery · Muscle activation and electromyography studies
