Imitation learning for long-horizon tasks is essential in enabling intelligent systems to efficiently acquire and general- ize complex behaviors with minimal reliance on supervision and hand-coded solutions. However, existing approaches often focus on short, isolated skills, require large amounts of demonstrations, and struggle to generalize to new tasks or shifts in the data distribution. We propose a novel neuro-symbolic framework that combines continuous control learning with symbolic domain ab- straction, requiring only a few skill demonstrations to effectively solve long-horizon tasks. Our results demonstrate high data efficiency, robust zero and few-shot generalization, and interpretable decision- making, paving the way for scalable, human-taught robotics.
@inproceedings{lorangetal25rss,
title={Few-Shot Neuro-Symbolic Imitation Learning for Long-Horizon Planning and Acting},
author={Pierrick Lorrang and Helen Lu and Johannes Huemer and Patrick Zips and Matthias Scheutz},
year={2025},
booktitle={Proceedings of CoRL},
url={https://hrilab.tufts.edu/publications/lorangetal25rss.pdf}
}