ReLAM: Learning Anticipation Model for Rewarding Visual Robotic Manipulation

Nan Tang, Jing-Cheng Pang, Guanlin Li +2

Reward design remains a critical bottleneck in visual reinforcement learning (RL) for robotic manipulation. In simulated environments, rewards are conventionally designed based on…