Ergodic Imitation for Adaptive Exploration around Demonstrations
A new paper proposes an adaptive ergodic imitation approach that constructs a target distribution from demonstration geometry to generate trajectories adaptively interpolating between tracking and exploration, addressing the mismatch between training and deployment in robot imitation learning.
[2605.13996] Ergodic Imitation for Adaptive Exploration around Demonstrations
[Submitted on 13 May 2026]
Title:Ergodic Imitation for Adaptive Exploration around Demonstrations
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Abstract: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.
Comments: 4 pages, 3 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.13996 [cs.RO]
(or arXiv:2605.13996v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.13996
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ziyi Xu [view email] [v1] Wed, 13 May 2026 18:06:46 UTC (887 KB)
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