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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…
Action Robust Reinforcement Learning via Optimal Adversary Aware Policy Optimization
Buqing Nie, Yangqing Fu, Jingtian Ji +1
Reinforcement Learning (RL) has achieved remarkable success in sequential decision tasks. However, recent studies have revealed the vulnerability of RL policies to different pertur…
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…
DyNaVLM: Zero-Shot Vision-Language Navigation System with Dynamic Viewpoints and Self-Refining Graph Memory
Zihe Ji, Huangxuan Lin, Yue Gao
We present DyNaVLM, an end-to-end vision-language navigation framework using Vision-Language Models (VLM). In contrast to prior methods constrained by fixed angular or distance int…
Stable CDE Autoencoders with Acuity Regularization for Offline Reinforcement Learning in Sepsis Treatment
Yue Gao
Effective reinforcement learning (RL) for sepsis treatment depends on learning stable, clinically meaningful state representations from irregular ICU time series. While previous wo…
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…
Select before Act: Spatially Decoupled Action Repetition for Continuous Control
Buqing Nie, Yangqing Fu, Yue Gao
Reinforcement Learning (RL) has achieved remarkable success in various continuous control tasks, such as robot manipulation and locomotion. Different to mainstream RL which makes d…