arXiv:2610.10297v1 Announce Type: cross
Abstract: While energy efficiency is a critical objective for legged-robot locomotion control, achieving low energy consumption while maintaining robust perfor...
By Ammar Issa, Anubhav Singh, Anton Tsaritsin, Sergey Kolyubin
arXiv:2606. 14270v1 Announce Type: cross Abstract: Fall recovery is critical for autonomous legged locomotion.
By Haidong Hou, Zhangguo Yu, Tao Han, Hengbo Qi, Khaleel Ghazal, Yu Zhang, Yidong Du, Xuechao Chen, Fei Meng
arXiv:2603.26687v2 Announce Type: replace-cross
Abstract: Hybrid aerial--ground robots can use thrust to cross obstacles that impede wheel-driven motion, but deciding how much thrust to apply during...
By Jiaxing Li, Ishaan Bhimwal, Wen Tian, Xinhang Xu, Junbin Yuan, Yuxin Guo, Sebastian Scherer, Muqing Cao
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:2603. 12408v2 Announce Type: replace-cross Abstract: Motion imitation learning (IL) is increasingly used in robotics and human gait modeling, yet its ability to recover biomechanically consistent joint moments without explicit kinetic information remains unclear.
By Xinyi Liu, Jangwhan Ahn, Edgar Lobaton, Jennie Si, He Huang
The paper introduces a Reflex-Informed Neuromuscular Reinforcement Learning framework for muscle-driven locomotion. It combines a fixed phase-dependent reflex controller with a reinforcement learning policy that adjusts reflex gains and thresholds for hip swing, knee support, and ankle propulsion. Experiments show the method produces physiologically plausible walking with improved kinematic accuracy, dynamic consistency, symmetry, and stride consistency, and remains robust to muscle weakness and perturbations without retraining.
By Jian Zhou, Xingyu Zhang, Rui Ma, Yu Cao, Shane Xie, Zhi-qiang Zhang