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…
Learning-based controllers show promising performances in robotic control tasks. However, they still present potential safety risks due to the difficulty in ensuring satisfaction of complex action constraints. We propose a novel action-constrained reinforcement learning method, which transforms the