PhysioAI introduces a clinical knowledge‑guided framework that injects structured physiotherapy knowledge into skeleton‑based action recognition models. By combining graph‑based spatiotemporal modeling with semantic anchors derived from a Clinical Knowledge Dictionary encoded via a frozen CLIP model, PhysioAI improves training of skeleton representations while requiring only skeleton inputs at inference. In subject‑disjoint evaluations, it outperforms existing methods on KiMoRe, Hard‑67, and UI‑PRMD benchmarks, achieving up to 99.03% accuracy on KiMoRe overall.
By Jie Cao, Euijoon Ahn, Anwar Hassan, Jinman Kim
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
The paper presents a theoretical framework for human torque estimation-based task‑agnostic control of robotic exoskeletons, defining matched assistance as scenarios where the robot positively contributes to human movement. It designs the robot’s desired interaction torque to guarantee a lower bound on matched assistance probability across the entire torque distribution, including unseen data. Experimental validation on the ABLE upper‑limb exoskeleton shows that the strategy achieves smooth movement and reduced human effort across multiple tasks.
By Duy Hoang, Bastien Berret, Olivier Bruneau, Laurent Fribourg
arXiv:2606. 19728v1 Announce Type: cross Abstract: Infants are well known to develop their motor skills through dense interaction with caregivers.
By Rui Fukushima, Jun Tani
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:2608. 12145v1 Announce Type: cross Abstract: Autonomous rehabilitation systems must not only recognize human motion but also provide structured feedback to support users without continuous therapist supervision.
By Lara Pereira, Jo\~ao Ruivo Paulo, Pedro Santos, Paulo Peixoto
arXiv:2510. 12363v4 Announce Type: replace-cross Abstract: The pretraining-finetuning paradigm has facilitated numerous transformative advancements in artificial intelligence research in recent years.
By Jiale Fan, Andrei Cramariuc, Tifanny Portela, Marco Hutter
arXiv:2608. 14028v1 Announce Type: cross Abstract: Dexterous manipulation is a fundamental capability for embodied intelligence, but scaling it remains difficult because robot demonstrations are expensive to collect and action spaces vary across embodiments.
By Zhiyue Zhao, Jingyi Wu, Hairuo Liu, Mingyu Liu, Liyang Li, Hengdi Zhang, Tong He, Zhengxue Cheng
arXiv:2505. 03296v2 Announce Type: replace-cross Abstract: We present Mixture of Discrete-time Gaussian Processes (MiDiGap), a novel approach for flexible policy representation and imitation learning in robot manipulation.
By Jan Ole von Hartz, Adrian R\"ofer, Joschka Boedecker, Abhinav Valada
arXiv:2609.01596v1 Announce Type: cross
Abstract: Real-world robotic assembly at sub-millimeter tolerances demands spatial precision, compliant interaction, and robustness to contact failures. We pre...
By Haoyuan Deng, Haichao Liu, Wenkai Guo, Yuan Ling, Zaijia Yang, Yuanjiang Xue, Haosheng Sun, Liangzi Wang, Ziwei Wang
Real-world robotic assembly at sub-millimeter tolerances demands spatial precision, compliant interaction, and robustness to contact failures. We present Facet-0, a robotic foundation model that predi...
This paper introduces an energy-aware approach to robotic manipulation by defining a joint-space mechanical-work proxy based on joint torque and angular displacement. A differentiable energy predictor is trained to estimate this work from robot states and actions, enabling it to serve as a regularizer that fine‑tunes a pretrained manipulation policy. Applied to RVT‑2 on RLBench, the method reduces average mechanical work from 208.8 J to 204.4 J (a 2.1 % drop) while slightly improving task success from 86.2 % to 86.9 % across 12 manipulation tasks.
By Toshiki Otani, Hiromu Taketsugu, Norimichi Ukita