Planning safe robot motions in the presence of humans requires reliable forecasts of future human motion. However, simply predicting the most likely motion from prior interactions does not guarantee safety. Such forecasts fail to model the long tail of possible events, which are rarely observed in limited datasets. On the other hand, planning for worst-case motions leads to overtly conservative behavior and a "frozen robot". Instead, we aim to learn forecasts that predict counterfactuals that humans guard against. We propose a novel game-theoretic framework for joint planning and forecasting with the payoff being the performance of the planner against the demonstrator, and present practical algorithms to train models in an end-to-end fashion. We demonstrate that our proposed algorithm results in safer plans in a crowd navigation simulator and real-world datasets of pedestrian motion. We release our code at https://github.com/portal-cornell/Game-Theoretic-Forecasting-Planning.

翻译：在人类存在的环境中规划安全的机器人运动需要可靠的未来人类运动预测。然而，仅从先前的交互中预测最可能的运动并不能保证安全。此类预测未能建模可能事件的“长尾”分布，而这些事件在有限数据集中鲜有观测。另一方面，针对最坏情况运动进行规划会导致过度保守的行为和“冻结机器人”。为此，我们致力于学习能够预测人类所防范的反事实事件的预测模型。我们提出了一种新颖的博弈论框架，该框架将规划与预测联合进行，其收益为规划器相对演示者的性能表现，并提出了实用的端到端模型训练算法。实验表明，在人群导航模拟器与真实世界行人运动数据集上，我们提出的算法能够生成更安全的规划方案。我们已将代码开源至 https://github.com/portal-cornell/Game-Theoretic-Forecasting-Planning。