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器人等机器人日益在动态环境中与人类近距离物理接触。在此类场景下，机器人可利用人类运动预测器预测其未来状态，并规划安全高效的轨迹。然而，任何模型都不完美——当观察到的行人行为偏离模型预测时，机器人可能规划出不安全的机动动作。近期研究探索通过维护预测模型中的人类置信度参数来应对这一挑战，即根据观测到的人类行为在预测模型下的似然概率，在线调整预测的人类行为。这引出了一个新的研究课题，即：**当置信度参数变化时，如何在线计算未来人类状态？** 本文提出一种基于Hamilton-Jacobi（HJ）可达性的方法来解决该问题。我们将置信度参数视为系统中的虚拟状态，计算参数条件前向可达管集（FRT），该集合可提供作为置信度参数函数的未来人类状态。在线运行时，当置信度参数变化时，我们只需查询对应的FRT，并据此更新机器人规划。计算参数条件FRT对应一个（离线）高维可达性问题，我们通过利用数据驱动可达性分析的最新进展进行求解。整体而言，我们的框架能够在人类预测模型存在偏差的情况下，在线维护并更新人机交互场景中的安全性保证。我们在多个涉及最先进的深度学习预测模型的安全关键型自动驾驶场景中验证了该方法。