Learn and Transfer Knowledge of Preferred Assistance Strategies in Semi-Autonomous Telemanipulation
Learn and Transfer Knowledge of Preferred Assistance Strategies in Semi-Autonomous Telemanipulation
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DOI：
10.1007/s10846-022-01596-2
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发表时间：
2020-03
影响因子：
3.3
通讯作者：
Lingfeng Tao;Michael Bowman;Xu Zhou;Jiucai Zhang;Xiaoli Zhang
中科院分区：
文献类型：
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作者：
Lingfeng Tao;Michael Bowman;Xu Zhou;Jiucai Zhang;Xiaoli Zhang
使机器人能够提供有效的辅助，但仍然适应操作员的命令，用于远程操纵物体是非常具有挑战性的，因为机器人的辅助对于人类操作员来说并不总是直观的，并且人类的行为和偏好有时对于机器人来说是模糊的。由于手部结构的差异，来自机器人的一些运动辅助可能会使操作员感到意外，从而产生反直觉的运动，这可能会给人类带来更多的负担来纠正动作和/或降低操作员对系统控制的感觉。为了解决这些问题，我们开发了一种新的偏好感知辅助知识学习方法。辅助偏好模型学习人类偏好什么辅助，并且分阶段的模型更新方法在处理人类偏好数据的模糊性的同时确保学习稳定性。这样的偏好感知辅助知识使得远程操作的机器人手能够向操纵成功提供更主动但优选的辅助。我们还开发了知识转移方法，在不同的机器人手结构之间转移偏好知识，以避免大量的机器人特定的训练。已经进行了远程操纵3指手和2指手分别使用、移动和移交杯子的实验。结果表明，该方法使机器人能够有效地学习偏好知识，并允许机器人之间的知识转移，以较少的训练工作。
Enabling robots to provide effective assistance yet still accommodating the operator’s commands for telemanipulation of an object is very challenging because robot’s assistance is not always intuitive for human operators and human behaviors and preferences are sometimes ambiguous for the robot to interpret. Due to the difference in hand structures, some motion assistance from the robot may surprise the operator with counter-intuitive movements, which could introduce more burden to the human to correct the actions and/or reduce the operator’s sense of system control. To address these problems, we developed a novel preference-aware assistance knowledge learning approach. An assistance preference model learns what assistance is preferred by a human, and a stage-wise model updating method ensures the learning stability while dealing with the ambiguity of human preference data. Such a preference-aware assistance knowledge enables a teleoperated robot hand to provide more active yet preferred assistance toward manipulation success. We also developed knowledge transfer methods to transfer the preference knowledge across different robot hand structures to avoid extensive robot-specific training. Experiments to telemanipulate a 3-finger hand and 2-finger hand, respectively, to use, move, and hand over a cup have been conducted. Results demonstrated that the methods enabled the robots to effectively learn the preference knowledge and allowed knowledge transfer between robots with less training effort.