3 accepted papers
Humanoid loco-manipulation requires executing precise manipulation tasks while maintaining dynamic stability amid base motion and impacts. Existing approaches typically formulate commands in body-centric frames, fail to inherently correct cumulative world-frame drift induced by legged locomotion. We…
While flow-based generative models have demonstrated strong performance across a wide range of domains, deploying them in safety-critical physical systems remains challenging due to strict constraint requirements. Existing approaches typically enforce safety through post-hoc corrections, which incur…
In complex environments, adaptive and fault-tolerant capabilities are essential for legged robot locomotion. To address this challenge, this study proposes a reinforcement learning framework that integrates contrastive learning with forward prediction to achieve fault-tolerant locomotion for legged…