Planning for Change: Reinforcement Learning Combined with Bounded Extremum Seeking for Robotic Control under Distribution Shift
A hybrid controller combines deep reinforcement learning (DDPG) for fast initial task entry with bounded extremum seeking (ES) for robust adaptation during manipulation. This approach addresses performance degradation when test conditions differ from training, such as varying friction or time-varying goals. The system switches between RL and ES based on contact to leverage the strengths of both methods.