Deep Reinforcement Learning for Robotic Manipulation under Distribution Shift with Bounded Extremum Seeking
This addresses robustness issues for robotic manipulation in contact-rich tasks, but is incremental as it builds on existing methods.
The paper tackles the problem of reinforcement learning policies degrading under distribution shift in robotic manipulation tasks, and proposes a hybrid controller combining deep deterministic policy gradient with bounded extremum seeking, achieving improved robustness in out-of-distribution settings like time-varying goals and spatially varying friction patches.
Reinforcement learning has shown strong performance in robotic manipulation, but learned policies often degrade in performance when test conditions differ from the training distribution. This limitation is especially important in contact-rich tasks such as pushing and pick-and-place, where changes in goals, contact conditions, or robot dynamics can drive the system out-of-distribution at inference time. In this paper, we investigate a hybrid controller that combines reinforcement learning with bounded extremum seeking to improve robustness under such conditions. In the proposed approach, deep deterministic policy gradient (DDPG) policies are trained under standard conditions on the robotic pushing and pick-and-place tasks, and are then combined with bounded ES during deployment. The RL policy provides fast manipulation behavior, while bounded ES ensures robustness of the overall controller to time variations when operating conditions depart from those seen during training. The resulting controller is evaluated under several out-of-distribution settings, including time-varying goals and spatially varying friction patches.