Offline Learning of Counterfactual Predictions for Real-World Robotic Reinforcement Learning
Jun Jin,Daniel Graves,Cameron Haigh,Jun Luo,Martin Jagersand,Jun Jin,Daniel Graves,Cameron Haigh,Jun Luo,Martin Jagersand
We consider real-world reinforcement learning (RL) of robotic manipulation tasks that involve both visuomotor skills and contact-rich skills. We aim to train a policy that maps multimodal sensory observations (vision and force) to a manipulator's joint velocities under practical considerations. We propose to use offline samples to learn a set of general value functions (GVFs) that make counterfact...
