Efficient and Trustworthy Social Navigation via Explicit and Implicit Robot-Human Communication
Efficient and Trustworthy Social Navigation via Explicit and Implicit Robot-Human Communication
复制标题
DOI：
10.1109/tro.2020.2964824
复制
发表时间：
2020-06-01
影响因子：
7.8
通讯作者：
Sadigh, Dorsa
中科院分区：
文献类型：
--
作者：
Che, Yuhang;Okamura, Allison M.;Sadigh, Dorsa
在这篇文章中，我们提出了一个规划框架，使用隐式（机器人运动）和显式（视觉/听觉/触觉反馈）通信的组合在移动的机器人导航。首先，我们开发了一个模型，该模型近似于人类导航中的连续运动和离散行为模式，考虑了隐式和显式通信对人类决策的影响。该模型将人类近似为最佳代理，通过反向强化学习获得奖励函数。其次，规划者使用这个模型来生成最大化机器人的透明度和效率的交流动作。我们实现了一个移动的机器人上的规划，使用可穿戴式触觉设备进行显式通信。在室内人-机器人对正交交叉情况的用户研究中，机器人能够主动地将其意图传达给用户，以避免碰撞并促进有效的轨迹。结果表明，与简单地执行碰撞避免相比，规划器生成的计划更容易理解，减少了用户的工作量，并增加了用户对机器人的信任。这篇文章的主要贡献是整合和分析的显式通信（连同隐式通信）的社会导航。
In this article, we present a planning framework that uses a combination of implicit (robot motion) and explicit (visual/audio/haptic feedback) communication during mobile robot navigation. First, we developed a model that approximates both continuous movements and discrete behavior modes in human navigation, considering the effects of implicit and explicit communication on human decision-making. The model approximates the human as an optimal agent, with a reward function obtained through inverse reinforcement learning. Second, a planner uses this model to generate communicative actions that maximize the robot's transparency and efficiency. We implemented the planner on a mobile robot, using a wearable haptic device for explicit communication. In a user study of an indoor human-robot pair orthogonal crossing situation, the robot is able to actively communicate its intent to users in order to avoid collisions and facilitate efficient trajectories. Results show that the planner generated plans that are easier to understand, reduce users' effort, and increase users' trust of the robot, compared to simply performing collision avoidance. The key contribution of this article is the integration and analysis of explicit communication (together with implicit communication) for social navigation.