Learning Dense Rewards for Contact-Rich Manipulation Tasks
This addresses the challenge of reducing expert effort in reward design for robotics, though it is incremental as it builds on existing reward learning methods.
The paper tackles the problem of designing reward functions for contact-rich manipulation tasks by algorithmically extracting dense rewards from high-dimensional robot observations, achieving better performance and faster convergence than baselines on peg-in-hole and USB insertion tasks.
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 algorithmically from robots' high-dimensional observations, such as images and tactile feedback. In contrast to state-of-the-art high-dimensional reward learning methodologies, our approach does not leverage adversarial training, and is thus less prone to the associated training instabilities. Instead, our approach learns rewards by estimating task progress in a self-supervised manner. We demonstrate the effectiveness and efficiency of our approach on two contact-rich manipulation tasks, namely, peg-in-hole and USB insertion. The experimental results indicate that the policies trained with the learned reward function achieves better performance and faster convergence compared to the baselines.