Learning Dense Rewards for Contact-Rich Manipulation Tasks
Zheng Wu Wenzhao Lian Vaibhav Unhelkar Masayoshi Tomizuka Stefan Schaal Zheng Wu Wenzhao Lian Vaibhav Unhelkar Masayoshi Tomizuka Stefan Schaal
Rewards play a crucial role in reinforcement learning. To arrive at the desired policy, the design of a suitable reward function often requires significant domain expertise as well as trial-and-error. Here, we aim to minimize the effort involved in designing reward functions for contact-rich manipulation tasks. In particular, we provide an approach capable of extracting dense reward functions algo...