{"paper":{"title":"SkillWrapper: Generative Predicate Invention for Task-level Robot Planning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A formal theory of generative predicate invention produces symbolic operators for provably sound and complete robot task planning from RGB images.","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Ahmed Jaafar, Benned Hedegaard, David Paulius, George Konidaris, Haotian Fu, Naman Shah, Shreyas S. Raman, Skye Thompson, Stefanie Tellex, Yichen Wei, Ziyi Yang","submitted_at":"2025-11-22T22:25:11Z","abstract_excerpt":"Generalizing from individual skill executions to long-horizon tasks is a core challenge in building autonomous robots. A promising direction is learning high-level, symbolic representations of low-level robot skills, enabling abstract reasoning independent of the low-level state space. Recent advances in foundation models have made it possible to generate symbolic predicates that operate on raw sensory inputs-a process we call generative predicate invention-to facilitate downstream representation learning. However, prior work learns these abstractions using heuristic or ad-hoc procedures, igno"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We address both questions by presenting a formal theory of generative predicate invention for skill abstraction, resulting in symbolic operators that can be used for provably sound and complete planning.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The predicates generated by the foundation model satisfy the formal properties (e.g., completeness and soundness conditions) required by the theory, and these properties transfer from simulation or collected data to real-robot execution with black-box skills.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"SkillWrapper learns human-interpretable symbolic representations of robot skills from images via foundation models, yielding operators for provably sound and complete planning on long-horizon tasks.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A formal theory of generative predicate invention produces symbolic operators for provably sound and complete robot task planning from RGB images.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"fab309c5c094b3b32384c9e3ef5ee4595e3bb63a3dcbc11be117f2c9f4f67f70"},"source":{"id":"2511.18203","kind":"arxiv","version":7},"verdict":{"id":"a69b6e03-4cfd-444c-af80-b82b2ce4d9f2","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T05:39:17.859665Z","strongest_claim":"We address both questions by presenting a formal theory of generative predicate invention for skill abstraction, resulting in symbolic operators that can be used for provably sound and complete planning.","one_line_summary":"SkillWrapper learns human-interpretable symbolic representations of robot skills from images via foundation models, yielding operators for provably sound and complete planning on long-horizon tasks.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The predicates generated by the foundation model satisfy the formal properties (e.g., completeness and soundness conditions) required by the theory, and these properties transfer from simulation or collected data to real-robot execution with black-box skills.","pith_extraction_headline":"A formal theory of generative predicate invention produces symbolic operators for provably sound and complete robot task planning from RGB images."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2511.18203/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}