Primitive Skill-Based Robot Learning from Human Evaluative Feedback
Ayano Hiranaka,Minjune Hwang,Sharon Lee,Chen Wang,Li Fei-Fei,Jiajun Wu,Ruohan Zhang,Ayano Hiranaka,Minjune Hwang,Sharon Lee,Chen Wang,Li Fei-Fei,Jiajun Wu,Ruohan Zhang
Reinforcement learning (RL) algorithms face significant challenges when dealing with long-horizon robot manipulation tasks in real-world environments due to sample inefficiency and safety issues. To overcome these challenges, we propose a novel framework, SEED, which leverages two approaches: reinforcement learning from human feedback (RLHF) and primitive skill-based reinforcement learning. Both a...
