Constrained-Space Optimization and Reinforcement Learning for Complex Tasks
This work addresses the challenge of enabling robots to perform complex tasks like suturing more efficiently than human experts, though it appears incremental as it builds on existing learning from demonstration and reinforcement learning methods.
The paper tackled the problem of transferring manipulation skills from imperfect human demonstrations to robots under safety constraints, and the result was a learned policy that outperformed expert demonstrations in robotic suturing by improving motion smoothness and reducing task completion time.
Learning from Demonstration is increasingly used for transferring operator manipulation skills to robots. In practice, it is important to cater for limited data and imperfect human demonstrations, as well as underlying safety constraints. This paper presents a constrained-space optimization and reinforcement learning scheme for managing complex tasks. Through interactions within the constrained space, the reinforcement learning agent is trained to optimize the manipulation skills according to a defined reward function. After learning, the optimal policy is derived from the well-trained reinforcement learning agent, which is then implemented to guide the robot to conduct tasks that are similar to the experts' demonstrations. The effectiveness of the proposed method is verified with a robotic suturing task, demonstrating that the learned policy outperformed the experts' demonstrations in terms of the smoothness of the joint motion and end-effector trajectories, as well as the overall task completion time.