Abstract Robot position/force control provides an interaction scheme between the robot and the environment. When the environment is unknown, learning algorithms are needed. But, the learning space and learning time are big. To balance the learning accuracy and the learning time, we propose a hybrid reinforcement learning method, which can be in both discrete and continuous domains. The discrete-time learning has poor learning accuracy and less learning time. The continuous-time learning is slow but has better learning precision. This hybrid reinforcement learning learns the optimal contact force, meanwhile it minimizes the position error in the unknown environment. Convergence of the proposed learning algorithm is proven. Real-time experiments are carried out using the pan and tilt robot and the force/torque sensor.

中文翻译：

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使用混合强化学习在未知环境中控制机器人位置/力

摘要 机器人位置/力控制提供了机器人与环境之间的交互方案。当环境未知时，需要学习算法。但是，学习空间和学习时间都很大。为了平衡学习精度和学习时间，我们提出了一种混合强化学习方法，它可以在离散域和连续域中使用。离散时间学习的学习精度较差，学习时间较短。连续时间学习速度慢，但学习精度更好。这种混合强化学习学习最佳接触力，同时最大限度地减少未知环境中的位置误差。证明了所提出的学习算法的收敛性。使用平移和倾斜机器人和力/扭矩传感器进行实时实验。