1 citations · 1 across the 7 of their papers we have counts for
8 papers
E2DRL: End-to-End Differentiable Reinforcement Learning for 3D Obstacle Avoidance in Quadruped Robots
Cheng Fan, Bingyang Zhou, Gang Yang +7
Differentiable reinforcement learning shows strong promise for robot policy learning, but its application to legged locomotion has remained limited to blind walking or simulated 2D…
Enabling Robust Cloth Manipulation via Inference-Time Simulator-in-the-Loop Refinement
Xin Liu, Yulin Li, Ziming Li +7
Simulator-in-the-loop optimization offers a promising inference-time mechanism for robot manipulation. It uses a physical simulator as a backend rollout engine to evaluate candidat…
RARM: Confidence-Gated Progress Reward Modeling for RL in Manipulation
Pengzhi Yang, Xinyu Wang, Pengyu Jing +7
Reinforcement learning for robot manipulation is often bottlenecked by reward design, especially in long-horizon tasks: sparse success rewards provide weak supervision, while hand-…
FLASH: Fast Learning via GPU-Accelerated Simulation for High-Fidelity Deformable Manipulation in Minutes
Siyuan Luo, Bingyang Zhou, Chong Zhang +9
Simulation frameworks such as Isaac Sim have enabled scalable robot learning for locomotion and rigid-body manipulation; however, contact-rich simulation remains a major bottleneck…
Scalable Trajectory Generation for Whole-Body Mobile Manipulation
Yida Niu, Xinhai Chang, Xin Liu +2
Robots deployed in unstructured environments must coordinate whole-body motion -- simultaneously moving a mobile base and arm -- to interact with the physical world. This coupled m…
Learning Natural and Robust Hexapod Locomotion over Complex Terrains via Motion Priors based on Deep Reinforcement Learning
Xin Liu, Jinze Wu, Yinghui Li +3
Multi-legged robots offer enhanced stability to navigate complex terrains with their multiple legs interacting with the environment. However, how to effectively coordinate the mult…
PPL: Point Cloud Supervised Proprioceptive Locomotion Reinforcement Learning for Legged Robots in Crawl Spaces
Bida Ma, Nuo Xu, Chenkun Qi +4
Legged locomotion in constrained spaces (called crawl spaces) is challenging. In crawl spaces, current proprioceptive locomotion learning methods are difficult to achieve traverse…
Experience-Learning Inspired Two-Step Reward Method for Efficient Legged Locomotion Learning Towards Natural and Robust Gaits
Yinghui Li, Jinze Wu, Xin Liu +2
Multi-legged robots offer enhanced stability in complex terrains, yet autonomously learning natural and robust motions in such environments remains challenging. Drawing inspiration…