Robots such as autonomous vehicles and assistive manipulators are increasingly operating in dynamic environments and close physical proximity to people. In such scenarios, the robot can leverage a human motion predictor to predict their future states and plan safe and efficient trajectories. However, no model is ever perfect -- when the observed human behavior deviates from the model predictions, the robot might plan unsafe maneuvers. Recent works have explored maintaining a confidence parameter in the human model to overcome this challenge, wherein the predicted human actions are tempered online based on the likelihood of the observed human action under the prediction model. This has opened up a new research challenge, i.e., \textit{how to compute the future human states online as the confidence parameter changes?} In this work, we propose a Hamilton-Jacobi (HJ) reachability-based approach to overcome this challenge. Treating the confidence parameter as a virtual state in the system, we compute a parameter-conditioned forward reachable tube (FRT) that provides the future human states as a function of the confidence parameter. Online, as the confidence parameter changes, we can simply query the corresponding FRT, and use it to update the robot plan. Computing parameter-conditioned FRT corresponds to an (offline) high-dimensional reachability problem, which we solve by leveraging recent advances in data-driven reachability analysis. Overall, our framework enables online maintenance and updates of safety assurances in human-robot interaction scenarios, even when the human prediction model is incorrect. We demonstrate our approach in several safety-critical autonomous driving scenarios, involving a state-of-the-art deep learning-based prediction model.

翻译：诸如自动驾驶车辆和辅助机械臂等机器人，正越来越多地在动态环境中与人类近距离共处。在此类场景中，机器人可利用人体运动预测器预测其未来状态，从而规划安全高效的轨迹。然而，任何模型都存在局限性——当观测到的人类行为偏离模型预测时，机器人可能规划出非安全的操作。近期研究探索通过维护人类模型中的置信度参数来应对这一挑战：基于预测模型下观测到的人类动作的似然性，在线调整预测结果。这引出了新的研究课题，即**如何在置信度参数变化时在线计算人类的未来状态？**本文提出一种基于Hamilton-Jacobi可达性的方法解决该问题。通过将置信度参数视为系统虚拟状态，我们计算参数条件化的前向可达管（FRT），该管提供人类未来状态作为置信度参数的函数。在线运行时，随置信度参数变化，可直接查询对应FRT并更新机器人规划。参数条件化FRT的计算对应一个高维可达性离线问题，我们借助数据驱动可达性分析的最新进展进行求解。总体而言，本文框架可在人类预测模型不准确的人机交互场景中，实现安全保障的在线维护与更新。我们通过包含基于深度学习的先进预测模型的安全关键型自动驾驶场景验证了该方法的有效性。