Smith Trevor, Chen Yuhao, Hewitt Nathan, Hu Boyi, Gu Yu
Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV USA.
Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL USA.
Int J Soc Robot. 2023;15(4):661-678. doi: 10.1007/s12369-021-00795-5. Epub 2021 Jul 5.
In order to navigate safely and effectively with humans in close proximity, robots must be capable of predicting the future motions of humans. This study first consolidates human studies in motion, intention, and preference into a discretized human model that can readily be used in robotics decision making algorithms. Cooperative Markov Decision Process (Co-MDP), a novel framework that improves upon Multiagent MDPs, is then proposed for enabling socially aware robot obstacle avoidance. Utilizing the consolidated and discretized human model, Co-MDP allows the system to (1) approximate rational human behavior and intention, (2) generate socially-aware robotic obstacle avoidance behavior, and (3) remain robust to the uncertainty of human intention and motion variance. Simulations of a human-robot co-populated environment verify Co-MDP as a feasible obstacle avoidance algorithm. In addition, the anthropomorphic behavior of Co-MDP was assessed and confirmed with a human-in-the-loop experiment. Results reveal that participants can not directly differentiate agents that were controlled by human operators from Co-MDP, and the reported confidences of their choices indicates that the predictions from participants were backed by behavioral evidence rather than random guesses. Thus the main contributions for this paper are: consolidating past human studies of rational human behavior and intention into a simple, discretized model; the development of Co-MDP: a robotic decision framework that can utilize this human model and maximize the joint utility between the human and robot; and an experimental design for evaluation of the human acceptance of obstacle avoidance algorithms.
为了在人类近距离存在的情况下安全有效地导航，机器人必须能够预测人类的未来动作。本研究首先将关于人类运动、意图和偏好的研究整合为一个离散化的人类模型，该模型可直接用于机器人决策算法。然后提出了合作马尔可夫决策过程（Co-MDP），这是一个在多智能体马尔可夫决策过程基础上改进的新框架，用于实现具有社会意识的机器人避障。利用整合后的离散化人类模型，Co-MDP使系统能够（1）近似人类的理性行为和意图，（2）生成具有社会意识的机器人避障行为，以及（3）对人类意图的不确定性和运动变化保持鲁棒性。对人机共存环境的模拟验证了Co-MDP作为一种可行的避障算法。此外，通过人在回路实验评估并确认了Co-MDP的拟人行为。结果表明，参与者无法直接区分由人类操作员控制的智能体和Co-MDP控制的智能体，并且他们报告的选择置信度表明参与者的预测有行为证据支持，而非随机猜测。因此，本文的主要贡献在于：将过去关于人类理性行为和意图的研究整合为一个简单的离散化模型；开发Co-MDP：一种能够利用该人类模型并最大化人与机器人之间联合效用的机器人决策框架；以及设计一个用于评估人类对避障算法接受度的实验。
