Ziang Zheng

Transferable Latent-To-Latent Locomotion Policy for Efficient and Versatile Motion Control of Diverse Legged Robots

Reinforcement learning (RL) has demonstrated remarkable capability in acquiring robot skills, but learning each new skill still requires substantial data collection for training. The pretrain-and-finetune paradigm offers a promising approach for efficiently adapting to new robot entities and tasks.