围绕示范的自适应探索的遍历模仿
Ergodic Imitation for Adaptive Exploration around Demonstrations
AI总结：
本文提出一种自适应遍历模仿方法，通过构建演示的几何目标分布，生成适应性插值轨迹，解决机器人模仿学习中训练与部署条件不匹配的问题。
摘要：
在机器人领域，模仿学习中常见的挑战是训练与部署条件之间的不匹配，例如环境变化或不完美的观测和控制。当机器人在不匹配条件下跟随名义轨迹时，可能会卡住并无法完成任务。为此，我们提出了一种自适应遍历模仿方法，通过从检索的演示几何构造目标分布，并利用该分布生成适应性插值轨迹，以在演示基础上保持地面。我们的方法扩展了遍历控制的传统作用，将其应用于基于检索的递推时间框架中，以实现自适应模仿。
英文摘要：
In robotics, a common challenge in imitation learning is the mismatch between training and deployment conditions, caused, for example, by environmental changes or imperfect observation and control. When a robot follows a nominal trajectory under such mismatch, it may become stuck and fail to complete the task. This calls for adaptive online exploration strategies that remain grounded in demonstrations. To this end, we propose an adaptive ergodic imitation approach that constructs a target distribution from the geometry of the retrieved demonstrations and uses it to generate trajectories that adaptively interpolate between tracking and exploration. Our method extends ergodic control beyond its traditional role in area-coverage and search by incorporating demonstrations into a retrieval-based receding-horizon framework for adaptive imitation.
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。
