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Creators/Authors contains: "Stengel-Eskin, E"

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  1. ; ; (, Proceedings of the Nations of Americas Chapter of the Association for Computational Linguistics)
    Free, publicly-accessible full text available April 29, 2026
  2. ; ; ; ; ; ; (, Proceedings of the Computer Vision and Pattern Recognition Conference)
    Free, publicly-accessible full text available June 13, 2026
  3. ; ; ; ; ; (, Proceedings of the International Conference on Learning Representations)
    Free, publicly-accessible full text available April 24, 2026
  4. ; ; ; ; ; ; (, Proceedings of the 41st International Conference on Machine Learning)
    We present an algorithm for skill discovery from expert demonstrations. The algorithm first utilizes Large Language Models (LLMs) to propose an initial segmentation of the trajectories. Following that, a hierarchical variational inference framework incorporates the LLM-generated segmentation information to discover reusable skills by merging trajectory segments. To further control the trade-off between compression and reusability, we introduce a novel auxiliary objective based on the Minimum Description Length principle that helps guide this skill discovery process. Our results demonstrate that agents equipped with our method are able to discover skills that help accelerate learning and outperform baseline skill learning approaches on new long-horizon tasks in BabyAI, a grid world navigation environment, as well as ALFRED, a household simulation environment. 
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    Accepted Manuscript Full Text Available