Socially Aware Robot Obstacle Avoidance Considering Human Intention and Preferences.
Socially Aware Robot Obstacle Avoidance Considering Human Intention and Preferences.
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DOI：
10.1007/s12369-021-00795-5
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发表时间：
2023
影响因子：
4.7
通讯作者：
Gu Y
中科院分区：
文献类型：
--
作者：
Smith T;Chen Y;Hewitt N;Hu B;Gu Y
为了安全有效地与人类进行近距离导航，机器人必须能够预测人类未来的运动。这项研究首先将人类在运动、意图和偏好方面的研究整合到一个离散的人类模型中，该模型可以很容易地用于机器人决策算法。合作马尔可夫决策过程（Cooperative Markov Decision Process, Co-MDP）是一种改进多智能体mdp的新框架，用于实现具有社会意识的机器人避障。利用整合和离散的人类模型，Co-MDP允许系统(1)近似理性的人类行为和意图，(2)产生具有社会意识的机器人避障行为，以及(3)对人类意图和运动变化的不确定性保持鲁棒性。仿真结果验证了Co-MDP是一种可行的避障算法。此外，通过人在环实验对Co-MDP的拟人化行为进行了评估和确认。结果表明，参与者不能直接区分由人工操作员控制的代理和Co-MDP，他们选择的报告置信度表明参与者的预测是由行为证据支持的，而不是随机猜测。因此，本文的主要贡献在于：将过去对人类理性行为和意图的研究整合为一个简单的离散模型；开发Co-MDP：一个机器人决策框架，可以利用这一人类模型并最大化人与机器人之间的联合效用；并设计了一个实验来评估人类对避障算法的接受程度。
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.
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