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.

为了在近距离接触人类的情况下安全有效地导航，机器人必须能够预测人类未来的动作。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化的人类模型中，该模型可以很容易地用于机器人决策算法。协作马尔可夫决策过程 (Co-MDP) 是一种改进多代理 MDP 的新框架，然后提出用于实现具有社会意识的机器人避障。利用合并和离散化的人类模型，Co-MDP 允许系统（1）近似理性的人类行为和意图，（2）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保持稳健和运动方差。人机共生环境的模拟验证了 Co-MDP 作为一种可行的避障算法。此外，Co-MDP 的拟人化行为通过人在回路实验进行了评估和确认。结果表明，参与者无法直接区分由人类操作员控制的代理与 Co-MDP，报告的他们选择的置信度表明参与者的预测得到了行为证据的支持，而不是随机猜测。因此，本文的主要贡献是：Co-MDP 的开发：一种机器人决策框架，可以利用这种人类模型并最大化人与机器人之间的联合效用；