Yule Mo — accepted AI-conference papers

2026

MLM: Learning Multi-Task Loco-Manipulation Whole-Body Control for Quadruped Robot With Arm

Xin Liu, Bida Ma, Chenkun Qi, Yan Ding, Nuo Xu, Zhaxizhuoma +11

RA-L 2026

Whole-body loco-manipulation for quadruped robots with arms remains a challenging problem, particularly in achieving multi-task control. To address this, we propose MLM, a reinforcement learning framework driven by both real-world and simulation data. It enables a six-DoF robotic arm–equipped quadru

Cited by 4 Source ↗Scholar ↗

2026

PPL: Point Cloud Supervised Proprioceptive Locomotion Reinforcement Learning for Legged Robots in Crawl Spaces

Bida Ma, Nuo Xu, Chenkun Qi, Xin Liu, Yule Mo, Jinkai Wang +1

RA-L 2026

Legged locomotion in constrained spaces (called crawl spaces) is challenging. In crawl spaces, current proprioceptive locomotion learning methods are difficult to achieve traverse because only ground features are inferred. In this study, a point cloud supervised RL framework for proprioceptive locom

Cited by 1 Source ↗Scholar ↗