Interactive Plan Explicability in Human-Robot Teaming
Interactive Plan Explicability in Human-Robot Teaming
复制标题
人机协作中的交互计划可解释性
DOI：
10.1109/roman.2018.8525540
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Yu Zhang
中科院分区：
文献类型：
--
作者：
Mehrdad Zakershahrak;Akshay Sonawane;Ze Gong;Yu Zhang
人机协作是人工智能在快速发展的机器人领域中最重要的应用之一。为了实现有效的协作，机器人不仅必须维持其人类队友的行为模型以预测团队状态，还必须了解其人类队友对自身的期望。了解人类队友的期望会使机器人的行为更好地符合人类的期望，从而促进更高效且可能更安全的团队协作。我们的工作通过利用规划可解释性的概念，在序列领域中考虑此类队友模型来解决人机交互问题。然而，在规划可解释性中，人类仅仅被视为观察者。在本文中，我们扩展了规划可解释性，以考虑人类和机器人的行为能够相互影响的交互环境。我们将这种新的度量称为交互式规划可解释性（IPE）。我们将我们的方法在考虑此度量的情况下使用快速正向（FF）规划器生成的联合规划，与不考虑此度量的FF生成的规划，以及与人类受试者和运行FF规划器的机器人交互时创建的规划进行比较。由于当机器人的行为偏离人类受试者的期望时，预计人类受试者会动态地适应机器人的行为，所以预计与人类受试者一起创建的规划比FF规划更具可解释性，并且与我们的方法生成的可解释规划相当。结果表明，我们的算法生成的规划的可解释性得分确实比FF生成的规划更接近人类交互规划，这意味着我们的算法生成的规划在执行过程中与人类的预期规划更吻合。这在实践中能够导致更高效的协作。
Human-robot teaming is one of the most important applications of artificial intelligence in the fast-growing field of robotics. For effective teaming, a robot must not only maintain a behavioral model of its human teammates to project the team status, but also be aware of its human teammates' expectation of itself. Being aware of the human teammates' expectation leads to robot behaviors that better align with the human expectation, thus facilitating more efficient and potentially safer teams. Our work addresses the problem of human-robot interaction with the consideration of such teammate models in sequential domains by leveraging the concept of plan explicability. In plan explicability, however, the human is considered solely as an observer. In this paper, we extend plan explicability to consider interactive settings where the human and robot's behaviors can influence each other. We term this new measure Interactive Plan Explicability (IPE). We compare the joint plan generated by our approach with the consideration of this measure using the fast forward (FF) planner, with the plan generated by FF without such consideration, as well as with the plan created with human subjects interacting with a robot running an FF planner. Since the human subject is expected to adapt to the robot's behavior dynamically when it deviates from her expectation, the plan created with human subjects is expected to be more explicable than the FF plan, and comparable to the explicable plan generated by our approach. Results indicate that the explicability score of plans generated by our algorithm is indeed closer to the human interactive plan than the plan generated by FF, implying that the plans generated by our algorithms align better with the expected plans of the human during execution. This can lead to more efficient collaboration in practice.