让机器人在环境变化时仍能根据示范自适应探索，避免卡住
Ergodic Imitation for Adaptive Exploration around Demonstrations
- 基于示范几何构建目标分布，动态调节跟踪与探索
- 在未见过的环境中成功完成任务，避免因偏差而失败
- 适合需要在线调整的机器人操作场景
在机器人领域，模仿学习常因训练与部署条件不一致（如环境变化、观测或控制不完美）而失效。当机器人沿默认轨迹运行时，可能陷入困境无法完成任务。为此，我们提出一种自适应遍历模仿方法，通过检索到的示范几何构造目标分布，并在此基础上生成能在跟踪与探索间自适应切换的轨迹。该方法将遍历控制扩展至模仿学习，融入基于示范的滚动时域框架，使机器人在不确定环境下仍能保持对示范的依赖并主动探索。
原文摘要 · Abstract (English)
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.
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