基于迭代策略更新与对抗鲁棒共形预测的交互环境安全规划
Safe Planning in Interactive Environments via Iterative Policy Updates and Adversarially Robust Conformal Prediction
AI总结：
针对交互环境下自主智能体安全规划中分布偏移破坏共形预测假设的问题，本文提出结合迭代策略更新与对抗鲁棒共形预测的框架，提供可收敛的有效安全保证并通过实验验证。
摘要：
自主智能体在交互环境（例如在行人与人类驾驶车辆混杂场景中的自动驾驶车辆控制）中的安全规划是一项重大挑战，原因在于环境行为未知且会对自主智能体的行为做出反应。这种耦合会引发交互驱动的分布偏移：自主智能体的控制策略可能改变环境行为，进而导致现有研究中的安全保证失效。事实上，近期研究已采用共形预测（CP）方法，利用观测到的环境数据生成无分布依赖的安全保证。但在交互场景中，由于控制策略更新会改变环境行为、环境行为反过来又影响策略更新的循环依赖关系，CP对数据可交换性的假设被打破。为填补这一空白，本文提出一种迭代框架，通过量化计划中的策略更新对环境行为的潜在影响，在策略更新过程中鲁棒地维持安全保证。该框架通过对抗鲁棒CP实现：每个回合中，我们利用当前策略下的观测数据执行常规CP步骤，之后通过解析调整CP结果以应对分布偏移，从而在策略更新间迁移安全保证。该调整基于策略-轨迹灵敏度分析完成，最终得到安全的回合式开环规划器。本文进一步开展系统收缩分析，给出了CP结果与策略更新均能保证收敛的条件。我们在二维车辆-行人案例研究中通过实验验证了上述安全与收敛保证。据我们所知，这是首批在此类交互场景下提供有效安全保证的研究成果。
英文摘要：
Safe planning of an autonomous agent in interactive environments -- such as the control of a self-driving vehicle among pedestrians and human-controlled vehicles -- poses a major challenge as the behavior of the environment is unknown and reactive to the behavior of the autonomous agent. This coupling gives rise to interaction-driven distribution shifts where the autonomous agent's control policy may change the environment's behavior, thereby invalidating safety guarantees in existing work. Indeed, recent works have used conformal prediction (CP) to generate distribution-free safety guarantees using observed data of the environment. However, CP's assumption on data exchangeability is violated in interactive settings due to a circular dependency where a control policy update changes the environment's behavior, and vice versa. To address this gap, we propose an iterative framework that robustly maintains safety guarantees across policy updates by quantifying the potential impact of a planned policy update on the environment's behavior. We realize this via adversarially robust CP where we perform a regular CP step in each episode using observed data under the current policy, but then transfer safety guarantees across policy updates by analytically adjusting the CP result to account for distribution shifts. This adjustment is performed based on a policy-to-trajectory sensitivity analysis, resulting in a safe, episodic open-loop planner. We further conduct a contraction analysis of the system providing conditions under which both the CP results and the policy updates are guaranteed to converge. We empirically demonstrate these safety and convergence guarantees on a two-dimensional car-pedestrian case study. To the best of our knowledge, these are the first results that provide valid safety guarantees in such interactive settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
