Offline Reinforcement Learning via Sequence Modeling for Vision-Based Robotic Grasping

High cost of environmental interaction and low data efficiency limit the development of reinforcement learning in robotic grasping. This paper proposes an end-to-end robotic grasping method based on offline reinforcement learning via sequence modeling. It considers the most recent n-step history to assist the agent in making decisions, where a predictive model learns to directly predict actions from raw image inputs. The experimental results show that our method can achieve higher grasping success rate with less training data than traditional reinforcement learning algorithms in offline setting.