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.

中文翻译：

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通过具有事后经验回放的深度确定性策略梯度对假肢手部抓取任务进行持续共享控制

在现实生活中使用假手可能是一项艰巨的任务。截肢用户通常能够规划到达轨迹和手部抓握位置选择，但是在精确的手指运动方面失败，例如在没有过度用力的情况下使手指适应物体的表面。将这部分留给机器自主权会非常有效。为了将用户的意图和计划能力与机器人控制相结合，引入了共享控制，将用户的输入和机器人控制方法相结合以实现一个目标。共享控制问题可以表述为部分可观察的马尔可夫决策过程。寻找最优控制策略，我们采用自适应动态规划和基于强化学习的控制算法——深度确定性策略梯度结合事后经验回放。我们使用重新参数化技术提出了具有预测层的算法。该系统在修改后的模拟环境中进行了测试，以确保能够遵循用户的意图并将接触力保持在边界内以确保安全。