Jul 2026· 2026 23rd International Conference on Ubiquitous Robots (UR)· pp. 145-149· 0 citations· 35 references
Abstract
This paper proposes a reinforcement learning-based Impact Forces-Optimized Gait Control framework for achieving smooth and quiet humanoid locomotion. The proposed method integrates a quintic polynomial swing-foot trajectory designed to ensure zero vertical velocity and acceleration at foot-ground contact with a joint-jerk minimization reward. Impact forces are provided as privileged observations during training to improve robustness under an asymmetric information architecture. The effectiveness of the proposed method was verified through evaluations conducted in Isaac Lab and MuJoCo (Multi-Joint dynamics with Contact), demonstrating reduced impact forces across various locomotion modes. Additionally, velocity tracking evaluations confirm that the proposed method does not significantly degrade locomotion performance. Furthermore, its performance was validated on the G1 humanoid robot, where real-world walking tests confirmed a reduction in walking-induced acoustic noise.
A control method for reducing foot–ground contact noise during humanoid walking, achieving compliant contact and continuous regulation of locomotion noise by establishing a foot corner contact model along with a virtual compliance parameter is proposed.
The findings indicate that integrating reinforcement learning with Sigmoid parameter adaptation provides a systematic and effective solution for adaptive compliance regulation in mobile exoskeleton systems, enhancing adaptability, safety, and functional relevance for stroke patients undergoing lower limb rehabilitation...
A deep reinforcement learning approach for fault-tolerant locomotion under actuator power loss that employs an asymmetric actor-critic architecture in which the critic has access to privileged information during training, while the actor learns to reconstruct a corresponding latent representation from proprioceptive ob...
Giovanbattista Gravina, Luca Rossini, Carlo Rizzardo et al.· 1 citation
This research proposes a novel control architecture for dynamically stable ultra-slow quadruped locomotion on unstructured and slippery terrains. The approach is based on Imitation Learning (IL), where two neural controllers are trained to imitate an optimal Quadratic Programming (QP) controller derived from a Linear T...
Alex Li Noce, P. Manoonpong, L. Patanè et al.· International Conference on...· 0 citations
This paper explores the Underactuated Virtual Gravity (UVG) controller, a model-based control approach designed to achieve energy-efficient bipedal walking. The UVG controller minimizes actuator effort during level-ground walking by capitalizing on the inherent dynamics that facilitate stable passive gaits on downward...
Aikaterini Smyrli, E. Papadopoulos· 2026 IEEE/ASME International...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
