3 accepted papers
Diffusion models offer powerful generative capabilities for robot trajectory planning, yet their practical deployment on robots is hindered by a critical bottleneck: reliance on imitation learning from expert demonstrations. This paradigm is often impractical for specialized robots where data is sca…
Deep reinforcement learning (DRL) methods have shown significant promise in controlling the movement of quadruped robots. However, for systems like hexapod robots, which feature a higher-dimensional action space, it remains challenging for an agent to devise an effective control strategy directly. C
Reinforcement learning (RL) has demonstrated impressive performance in legged locomotion over various challenging environments. However, due to the sim-to-real gap and lack of explainability, unconstrained RL policies deployed in the real world still suffer from inevitable safety issues, such as joi