To provide safe and efficient services, robots must rely on observations from sensors (lidar, camera, etc.) to have a clear knowledge of the environment. In multi-agent scenarios, robots must further reason about the intrinsic motivation underlying the behavior of other agents in order to make inferences about their future behavior. Occlusions, which often occur in robot operating scenarios, make the decision-making of robots even more challenging. In scenarios without occlusions, dynamic game theory provides a solid theoretical framework for predicting the behavior of agents with different objectives interacting with each other over time. Prior work proposed an inverse dynamic game method to recover the game model that best explains observed behavior. However, an apparent shortcoming is that it does not account for agents that may be occluded. Neglecting these agents may result in risky navigation decisions. To address this problem, we propose a novel inverse dynamic game technique to infer the behavior of occluded, unobserved agents that best explains the observation of visible agents' behavior, and simultaneously to predict the agents' future behavior based on the recovered game model. We demonstrate our method in several simulated scenarios. Results reveal that our method robustly estimates agents' objectives and predicts trajectories for both visible and occluded agents from a short sequence of noise corrupted trajectory observation of only the visible agents.

翻译：为了提供安全高效的服务，机器人必须依靠传感器（激光雷达、相机等）的观测来清晰了解环境。在多智能体场景中，机器人需要进一步推理其他智能体行为背后的内在动机，以预测其未来行为。机器人运行场景中常见的遮挡问题，使得机器人的决策更具挑战性。在无遮挡场景下，动态博弈论为预测具有不同目标的智能体随时间交互的行为提供了坚实的理论框架。先前的研究提出了一种逆动态博弈方法，以恢复最能解释观测行为的最优博弈模型。然而，一个明显的不足是，该方法并未考虑可能被遮挡的智能体。忽略这些智能体可能导致危险的导航决策。为解决这一问题，我们提出了一种新颖的逆动态博弈技术，用于推断被遮挡、未观测智能体的行为，使其能最优解释可见智能体的观测行为特征，并同时基于恢复的博弈模型预测智能体的未来行为。我们在多个模拟场景中验证了该方法。结果表明，本文方法能从仅包含可见智能体的短序列噪声轨迹观测中，稳健地估计智能体目标，并同时预测可见与遮挡智能体的轨迹。