5 papers
TTT-Parkour: Rapid Test-Time Training for Perceptive Robot Parkour
Shaoting Zhu, Baijun Ye, Jiaxuan Wang +5
Achieving highly dynamic humanoid parkour on unseen, complex terrains remains a challenge in robotics. Although general locomotion policies demonstrate capabilities across broad te…
Flexible Locomotion Learning with Diffusion Model Predictive Control
Runhan Huang, Haldun Balim, Heng Yang +1
Legged locomotion demands controllers that are both robust and adaptable, while remaining compatible with task and safety considerations. However, model-free reinforcement learning…
MoE-Loco: Mixture of Experts for Multitask Locomotion
Runhan Huang, Shaoting Zhu, Yilun Du +1
We present MoE-Loco, a Mixture of Experts (MoE) framework for multitask locomotion for legged robots. Our method enables a single policy to handle diverse terrains, including bars,…
VR-Robo: A Real-to-Sim-to-Real Framework for Visual Robot Navigation and Locomotion
Shaoting Zhu, Linzhan Mou, Derun Li +3
Recent success in legged robot locomotion is attributed to the integration of reinforcement learning and physical simulators. However, these policies often encounter challenges whe…
Robust Robot Walker: Learning Agile Locomotion over Tiny Traps
Shaoting Zhu, Runhan Huang, Linzhan Mou +1
Quadruped robots must exhibit robust walking capabilities in practical applications. In this work, we propose a novel approach that enables quadruped robots to pass various small o…