{"ID":2827251,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.17992","arxiv_id":"2512.17992","title":"Unifying Deep Predicate Invention with Pre-trained Foundation Models","abstract":"Long-horizon robotic tasks are hard due to continuous state-action spaces and sparse feedback. Symbolic world models help by decomposing tasks into discrete predicates that capture object properties and relations. Existing methods learn predicates either top-down, by prompting foundation models without data grounding, or bottom-up, from demonstrations without high-level priors. We introduce UniPred, a bilevel learning framework that unifies both. UniPred uses large language models (LLMs) to propose predicate effect distributions that supervise neural predicate learning from low-level data, while learned feedback iteratively refines the LLM hypotheses. Leveraging strong visual foundation model features, UniPred learns robust predicate classifiers in cluttered scenes. We further propose a predicate evaluation method that supports symbolic models beyond STRIPS assumptions. Across five simulated and one real-robot domains, UniPred achieves 2-4 times higher success rates than top-down methods and 3-4 times faster learning than bottom-up approaches, advancing scalable and flexible symbolic world modeling for robotics.","short_abstract":"Long-horizon robotic tasks are hard due to continuous state-action spaces and sparse feedback. Symbolic world models help by decomposing tasks into discrete predicates that capture object properties and relations. Existing methods learn predicates either top-down, by prompting foundation models without data grounding,...","url_abs":"https://arxiv.org/abs/2512.17992","url_pdf":"https://arxiv.org/pdf/2512.17992v1","authors":"[\"Qianwei Wang\",\"Bowen Li\",\"Zhanpeng Luo\",\"Yifan Xu\",\"Alexander Gray\",\"Tom Silver\",\"Sebastian Scherer\",\"Katia Sycara\",\"Yaqi Xie\"]","published":"2025-12-19T18:59:56Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}