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首页> 外文期刊>International Journal of Advanced Robotic Systems >Continuous shared control in prosthetic hand grasp tasks by Deep Deterministic Policy Gradient with Hindsight Experience Replay
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Continuous shared control in prosthetic hand grasp tasks by Deep Deterministic Policy Gradient with Hindsight Experience Replay

机译：通过深度确定性政策梯度与后敏感体验重放的持续共享控制掌握任务

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摘要

Grasp using a prosthetic hand in real life can be a difficult task. The amputee users are often capable of planning the reaching trajectory and hand grasp location selection, however, failed in precise finger movements, such as adapting the fingers to the surface of the object without excessive force. It is much efficient to leave that part to the machine autonomy. In order to combine the intention and planning ability of users with robotic control, the shared control is introduced in which users’ inputs and robot control methods are combined to achieve a goal. The shared control problem can be formulated as a Partially Observable Markov Decision Process. To find the optimal control policy, we adopt an adaptive dynamic programming and reinforcement learning-based control algorithm-Deep Deterministic Policy Gradient combined with Hindsight Experience Replay. We proposed the algorithm with a prediction layer using the reparameterization technique. The system was tested in a modified simulation environment for the ability to follow the user’s intention and keep the contact force in boundary for safety.
机译：掌握现实生活中的假肢可能是一项艰巨的任务。截肢者用户通常能够规划到达轨迹和手动抓握位置选择，但是，在精确的手指运动中失败，例如将手指调整到物体的表面而没有过大的力。将该部分留给机器自主权是有效的。为了结合有机机器人控制的意图和规划能力，引入了共享控制，其中将用户输入和机器人控制方法组合以实现目标。共享控制问题可以作为部分观察到的马尔可夫决策过程制定。为了找到最佳控制策略，我们采用了自适应动态规划和基于强化学习的控制算法 - 深度确定性政策梯度与后敏感体验重放相结合。我们使用Reparameterization技术提出了具有预测层的算法。该系统在修改的模拟环境中进行了测试，以便能够遵循用户的意图，并保持安全的边界中的接触力。

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