Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty
This addresses the challenge of enhancing robotic manipulation in uncertain environments, though it is incremental as it builds on residual policy learning.
The paper tackles the problem of improving existing controllers for contact-rich manipulation tasks under uncertainty by proposing a new residual feedback learning formulation that modifies both the output and feedback signals, achieving superior performance on a peg-insertion task with position and orientation uncertainty.
While classic control theory offers state of the art solutions in many problem scenarios, it is often desired to improve beyond the structure of such solutions and surpass their limitations. To this end, residual policy learning (RPL) offers a formulation to improve existing controllers with reinforcement learning (RL) by learning an additive "residual" to the output of a given controller. However, the applicability of such an approach highly depends on the structure of the controller. Often, internal feedback signals of the controller limit an RL algorithm to adequately change the policy and, hence, learn the task. We propose a new formulation that addresses these limitations by also modifying the feedback signals to the controller with an RL policy and show superior performance of our approach on a contact-rich peg-insertion task under position and orientation uncertainty. In addition, we use a recent Cartesian impedance control architecture as the control framework which can be available to us as a black-box while assuming no knowledge about its input/output structure, and show the difficulties of standard RPL. Furthermore, we introduce an adaptive curriculum for the given task to gradually increase the task difficulty in terms of position and orientation uncertainty. A video showing the results can be found at https://youtu.be/SAZm_Krze7U .