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.

中文翻译：

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复杂任务的受限空间优化和强化学习

从演示中学习越来越多地用于将操作员的操作技能转移到机器人上。在实践中，重要的是要满足有限的数据和不完善的人类演示，以及潜在的安全限制。本文提出了一种用于管理复杂任务的受限空间优化和强化学习方案。通过受限空间内的交互，强化学习代理被训练以根据定义的奖励函数优化操作技能。学习后，最优策略来自于训练有素的强化学习代理，然后被实施以指导机器人执行类似于专家演示的任务。通过机器人缝合任务验证了所提出方法的有效性，