Biological robot arm motion through reinforcement learning

The present paper discusses an optimal control method of biological robot arm which has redundancy of the mapping from the control input to the task goal. The control input space is divided into a couple of subspaces according to a priority order depending on the progress and stability of learning. In the proposed method, the search noise which is required for reinforcement learning is restricted within the first priority subspace. Then the constraint is relaxed with the progress of learning, and the search space extends to the second priority subspace in accordance with the history of learning. The method was applied to the musculoskeletal system as an example of biological control systems. Dynamic manipulation is obtained through reinforcement learning with no previous knowledge of the arm´s dynamics. The effectiveness of the proposed method is shown by computational simulation.