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arXiv · 2503.04931

Curiosity-Driven Imagination: Discovering Plan Operators and Learning Associated Policies for Open-World Adaptation

Pierrick Lorang·Hong Lu·Matthias Scheutz
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Abstract

Adapting quickly to dynamic, uncertain environments-often called "open worlds"-remains a major challenge in robotics. Traditional Task and Motion Planning (TAMP) approaches struggle to cope with unforeseen changes, are data-inefficient when adapting, and do not leverage world models during learning. We address this issue with a hybrid planning and learning system that integrates two models: a low level neural network based model that learns stochastic transitions and drives exploration via an Intrinsic Curiosity Module (ICM), and a high level symbolic planning model that captures abstract transitions using operators, enabling the agent to plan in an "imaginary" space and generate reward machines. Our evaluation in a robotic manipulation domain with sequential novelty injections demonstrates that our approach converges faster and outperforms state-of-the-art hybrid methods.

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Pierrick Lorang, Hong Lu, Matthias Scheutz. 2025-03-06. Curiosity-Driven Imagination: Discovering Plan Operators and Learning Associated Policies for Open-World Adaptation. https://arxiv.org/abs/2503.04931

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