基于触觉传感器和强化学习内在奖励的机械臂抓取方法

The method for manipulator grasping based on tactile sensor and reinforcement learning intrinsic reward

摘要:

触觉在机器人抓取过程中扮演着重要的角色，但在大多数强化学习任务中，触觉仅被用于拓展状态空间，其提供的位置和压力等信息很少被完全利用.针对该问题，同时受内在奖励机制启发，首先设计了一种“倒T”形传感器阵列布局；然后基于这种传感器阵列提出了新的内在激励方法，该方法根据机械臂末端与物体接触位置的不同，给予不同的重视程度，鼓励智能体以更有效的姿态来夹取物体；最后将该方法在仿真环境中进行测试，结果表明该方法在夹取椭球和圆球物体任务中收敛速度比最新的基准方法平均提高了约20%.

Abstract:

Although play an important role in the process of the robot grasping, haptics is only used to extend the state space, and the information provided by it such as position and pressure is rarely fully utilized in most reinforcement tasks. In order to solve the issues, inspired by the intrinsic reward mechanism, an intrinsic incentive method based on the “inverted T” array sensor is proposed. According to the position where the end effector of the robot touches the object, the method gives degrees of importance, and encourages the agent to achieve the goal with a more effective posture. Finally, the method was tested in the simulation environment, and the results showed that the speed of convergence of the method in the task gripping ellipsoid objects was about 20% faster than the latest benchmark method.