5 papers · 1 filter
Coordinated Humanoid Robot Locomotion with Symmetry Equivariant Reinforcement Learning Policy
Buqing Nie, Yang Zhang, Rongjun Jin +4
The human nervous system exhibits bilateral symmetry, enabling coordinated and balanced movements. However, existing Deep Reinforcement Learning (DRL) methods for humanoid robots n…
Keep on Going: Learning Robust Humanoid Motion Skills via Selective Adversarial Training
Yang Zhang, Zhanxiang Cao, Buqing Nie +6
Humanoid robots are expected to operate reliably over long horizons while executing versatile whole-body skills. Yet Reinforcement Learning (RL) motion policies typically lose stab…
Learning Motion Skills with Adaptive Assistive Curriculum Force in Humanoid Robots
Zhanxiang Cao, Yang Zhang, Buqing Nie +3
Learning policies for complex humanoid tasks remains both challenging and compelling. Inspired by how infants and athletes rely on external support--such as parental walkers or coa…
Minimizing Acoustic Noise: Enhancing Quiet Locomotion for Quadruped Robots in Indoor Applications
Zhanxiang Cao, Buqing Nie, Yang Zhang +1
Recent advancements in quadruped robot research have significantly improved their ability to traverse complex and unstructured outdoor environments. However, the issue of noise gen…
Disturbance-Aware Adaptive Compensation in Hybrid Force-Position Locomotion Policy for Legged Robots
Yang Zhang, Buqing Nie, Zhanxiang Cao +2
Reinforcement Learning (RL)-based methods have significantly improved the locomotion performance of legged robots. However, these motion policies face significant challenges when d…