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

Pierrick Lorang and Hong Lu and Matthias Scheutz

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.

@inproceedings{lorangetal25icra,
  title={Curiosity-Driven Imagination: Discovering Plan Operators and Learning Associated Policies for Open-World Adaptation},
  author={Pierrick Lorang and Hong Lu and Matthias Scheutz},
  year={2025},
  booktitle={Proceedings of the 2025 IEEE International Conference on Robotics and Automation},
  url={https://hrilab.tufts.edu/publications/lorangetal25icra.pdf}
}