[论文解读] Explicable Robot Planning as Minimizing Distance from Expected Behavior.

Anagha Kulkarni, Tathagata Chakraborti+4 more|arXiv (Cornell University)|Nov 16, 2016

Reinforcement Learning in Robotics参考文献 12被引用 18

一句话总结

本文提出一种方法，通过使用学习到的可解释性距离函数，最小化机器人计划与人类预期之间的距离，从而生成对人类可解释的机器人计划。该方法利用人类标注的计划评估数据来训练距离度量，并将其作为启发式函数集成到任意时间搜索算法中，在模拟的自动驾驶汽车领域显著提升了计划的可解释性。

ABSTRACT

In order for robots to be integrated effectively into human work-flows, it is not enough to address the question of autonomy but also how their actions or plans are being perceived by their human counterparts. When robots generate task plans without such considerations, they may often demonstrate what we refer to as inexplicable behavior from the point of view of humans who may be observing it. This problem arises due to the human observer's partial or inaccurate understanding of the robot's deliberative process and/or the model (i.e. capabilities of the robot) that informs it. This may have serious implications on the human-robot work-space, from increased cognitive load and reduced trust in the robot from the human, to more serious concerns of safety in human-robot interactions. In this paper, we propose to address this issue by learning a distance function that can accurately model the notion of explicability, and develop an anytime search algorithm that can use this measure in its search process to come up with progressively explicable plans. As the first step, robot plans are evaluated by human subjects based on how explicable they perceive the plan to be, and a scoring function called explicability distance based on the different plan distance measures is learned. We then use this explicability distance as a heuristic to guide our search in order to generate explicable robot plans, by minimizing the plan distances between the robot's plan and the human's expected plans. We conduct our experiments in a toy autonomous car domain, and provide empirical evaluations that demonstrate the usefulness of the approach in making the planning process of an autonomous agent conform to human expectations.

研究动机与目标

• 为解决人机协作中机器人行为难以解释的问题，该问题会削弱信任并增加认知负荷。
• 通过基于人类对机器人计划的评估结果，学习一种基于距离的函数来建模人类对计划可解释性的感知。
• 开发一种任意时间搜索算法，利用学习到的可解释性距离作为启发式函数，逐步生成更加可解释的机器人计划。
• 弥合机器人规划过程与人类在共享工作空间中对预期行为的心理模型之间的差距。

提出的方法

• 人类受试者评估机器人计划的可解释性感知程度，生成一组计划与人类预期之间距离的数据集。
• 从多种计划距离度量中，利用人类标注的数据学习一种称为可解释性距离的评分函数。
• 将学习到的可解释性距离作为启发式函数，集成到任意时间搜索算法中，以指导计划生成。
• 搜索算法通过最小化机器人计划与预期计划之间的距离，确保计划与人类预期保持一致。
• 在玩具级自动驾驶汽车领域对方法进行评估，以模拟真实世界中的人机交互场景。
• 通过将人类期望直接嵌入规划启发式函数，实现将以人为本的设计理念融入机器人规划。

实验结果

研究问题

• RQ1 在人机协作中，如何使机器人计划对人类观察者更具可解释性？
• RQ2 哪种距离度量最能捕捉人类对计划可解释性的感知？
• RQ3 学习到的可解释性距离函数能否从以人为本的视角提升机器人计划的质量？
• RQ4 将可解释性作为启发式函数，如何影响机器人规划的效率与质量？

主要发现

• 经人类评估验证，学习到的可解释性距离函数能有效建模人类对计划可解释性的感知。
• 由可解释性距离引导的任意时间搜索算法生成的计划，其可解释性评分始终高于基线方法。
• 在自动驾驶汽车模拟环境中，使用该方法生成的计划在与人类预期的对齐程度上表现出可测量的提升。
• 将人类期望整合到规划启发式函数中，可降低认知负荷并增强对机器人行为的信任。