时序逻辑模仿：从演示中学习满足规划的运动策略
Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations
AI总结：
针对从演示中学习方法在人在回路扰动下无法保证任务复现的问题，提出以模式为离散抽象、结合具模式不变性与目标可达性的运动策略的时序逻辑模仿方法，可模拟LTL指定的离散规划，对两级扰动鲁棒且保证任务成功。
摘要：
从演示中学习（LfD）已在长时域任务中取得成功。然而，当问题复杂度还包含人在回路扰动时，现有最先进方法无法保证任务的成功复现。在本研究中，我们发现这一挑战的根源在于所学的连续策略无法满足演示中隐含的离散规划。通过利用模式（而非子目标）作为离散抽象，并结合兼具模式不变性和目标可达性的运动策略，我们证明所学的连续策略能够模拟由线性时序逻辑（LTL）公式指定的任意离散规划。因此，该模仿器对任务级和运动级扰动均具有鲁棒性，且能保证任务成功完成。项目页面：https://yanweiw.github.io/tli/
英文摘要：
Learning from demonstration (LfD) has succeeded in tasks featuring a long time horizon. However, when the problem complexity also includes human-in-the-loop perturbations, state-of-the-art approaches do not guarantee the successful reproduction of a task. In this work, we identify the roots of this challenge as the failure of a learned continuous policy to satisfy the discrete plan implicit in the demonstration. By utilizing modes (rather than subgoals) as the discrete abstraction and motion policies with both mode invariance and goal reachability properties, we prove our learned continuous policy can simulate any discrete plan specified by a linear temporal logic (LTL) formula. Consequently, an imitator is robust to both task- and motion-level perturbations and guaranteed to achieve task success. Project page: https://yanweiw.github.io/tli/
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。
