Differential game theory for versatile physical human-robot interaction
Differential game theory for versatile physical human-robot interaction
复制标题
DOI：
10.1038/s42256-018-0010-3
复制
发表时间：
2019-01-01
影响因子：
23.8
通讯作者：
Burdet, E.
中科院分区：
文献类型：
--
作者：
Li, Y.;Carboni, G.;Burdet, E.
机器人需要估计并适应人类行为，尤其是当人类动态随时间变化时。现在，自适应博弈论控制器可以帮助机器人在完成任务时适应人类行为。过去几十年来，与人类接触工作的机器人数量激增。然而，到目前为止，这些接触机器人很少利用物理交互提供的机会，并且缺乏产生多种行为的系统方法。在这里，我们开发了一种交互式机器人控制器，能够理解人类用户的控制策略并对他们的动作做出最佳反应。我们证明，将观察者与微分博弈论控制器相结合可以引起两个伙伴之间的稳定交互，精确地识别彼此的控制律，并让他们以最小的努力成功地执行任务。对人类受试者的模拟和实验证明了这些特性，并说明了该控制器如何诱导不同的代表性交互策略。
Robots need to estimate and adapt to human behaviour, especially when human dynamics change over time. Now adaptive game theory controllers can help robots adapt to human behaviour in a reaching task.The last decades have seen a surge of robots working in contact with humans. However, until now these contact robots have made little use of the opportunities offered by physical interaction and lack a systematic methodology to produce versatile behaviours. Here, we develop an interactive robot controller able to understand the control strategy of the human user and react optimally to their movements. We demonstrate that combining an observer with a differential game theory controller can induce a stable interaction between the two partners, precisely identify each other's control law, and allow them to successfully perform the task with minimum effort. Simulations and experiments with human subjects demonstrate these properties and illustrate how this controller can induce different representative interaction strategies.