具有未知动力学的机会约束运动规划的共形约束收紧

中文摘要 AI 辅助

运动规划算法计算控制序列，驱使自主机器人到达目标区域并避开不安全状态。现有方法通常仅针对标称模型或模拟器提供任务完成保证，当真实动力学未知或难以准确建模时可能失效。本文针对具有未知动力学且有可用近似标称模型的系统解决此限制，提出一种与规划器无关的约束收紧程序，为现有规划器提供关于真实系统的概率任务完成保证。利用共形预测给出标称到真实轨迹偏差的概率界，用该界收紧规划约束，并表明在标称模型下解决收紧问题是在真实系统上以规定概率解决原始问题的充分条件。通过实验验证了理论保证，并展示了相对于标称模型规划显著提高的任务完成率。

英文摘要

Motion planning algorithms compute control sequences that drive autonomous robots to goal regions while avoiding unsafe states. Existing methods, from sampling-based planning to deep reinforcement learning, typically provide task-completion guarantees only with respect to a nominal model or simulator, which may be invalidated when the true dynamics are unknown or difficult to model accurately. This letter addresses this limitation for systems with unknown dynamics and an available approximate nominal model, contributing a planner-agnostic constraint-tightening procedure that equips existing planners with a probabilistic task-completion guarantee on the true system. We leverage conformal prediction to provide a probabilistic bound on the nominal-to-true trajectory deviation over a distribution of planning problems. We tighten the planning constraints using that bound, and show that solving the tightened problem under the nominal model is a sufficient condition for solving the original problem on the true system with a prescribed probability. We validate the theoretical guarantees empirically and demonstrate substantially improved task completion relative to nominal-model planning.

发表机构

• Washington University in St. Louis（圣路易斯华盛顿大学）
• Arizona State University（亚利桑那州立大学）

机构由 AI 辅助整理，请以论文原文为准。