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
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
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...
By Yun Chen, Oluwasegun T. Akinniyi, Qiang Zhang
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
arXiv:2508. 18066v2 Announce Type: replace-cross Abstract: Controlling high-dimensional and nonlinear musculoskeletal models of the human body is a foundational scientific challenge.
By Boshi An, Alberto Silvio Chiappa, Merkourios Simos, Chengkun Li, Alexander Mathis
This study introduces a transformer-based model that personalizes robot-mediated rehabilitation by mapping surface electromyography (sEMG) signals to contact force trajectories. Using a transformer encoder-decoder with rotary positional embeddings and flow matching, the model predicts trajectories with less than 8% error on unseen tasks and users, and can be fine-tuned within a single therapy session. Live tests show participants achieve target neuromotor activations with only 15% error, demonstrating the feasibility of activation-targeted trajectory prescription.
By Gabriel Parra, Kimia Khoshnami, Daniel S. Brown, Laura A. Hallock