{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HLWTCXRB6MT75FEVTH2ETFGYEP","short_pith_number":"pith:HLWTCXRB","schema_version":"1.0","canonical_sha256":"3aed315e21f327fe949599f44994d823edf059f72a43197966f9fbae66433a5e","source":{"kind":"arxiv","id":"2401.03701","version":1},"attestation_state":"computed","paper":{"title":"ExTraCT -- Explainable Trajectory Corrections from language inputs using Textual description of features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"J-Anne Yow, Manoj Ramanathan, Neha Priyadarshini Garg, Wei Tech Ang","submitted_at":"2024-01-08T07:13:26Z","abstract_excerpt":"Natural language provides an intuitive and expressive way of conveying human intent to robots. Prior works employed end-to-end methods for learning trajectory deformations from language corrections. However, such methods do not generalize to new initial trajectories or object configurations. This work presents ExTraCT, a modular framework for trajectory corrections using natural language that combines Large Language Models (LLMs) for natural language understanding and trajectory deformation functions. Given a scene, ExTraCT generates the trajectory modification features (scene-specific and sce"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2401.03701","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-01-08T07:13:26Z","cross_cats_sorted":[],"title_canon_sha256":"da3da9131c6ca311daf43df8bb67eb1b42efe364ea50461d3766b0fe29d057b4","abstract_canon_sha256":"a5aa2896691b3f3de26df4bb12233c609b8ec0c68e59127a348726387b21afc1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:31:12.828378Z","signature_b64":"GYxTdjVJgbjfYiUvJ4cNV0nLSs4U1OiD3KvC5HCh5C/XbZ/bk4ThESW8WnBQsnai4U0obN8qLceG8T7XwcUzCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3aed315e21f327fe949599f44994d823edf059f72a43197966f9fbae66433a5e","last_reissued_at":"2026-07-05T07:31:12.827896Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:31:12.827896Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ExTraCT -- Explainable Trajectory Corrections from language inputs using Textual description of features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"J-Anne Yow, Manoj Ramanathan, Neha Priyadarshini Garg, Wei Tech Ang","submitted_at":"2024-01-08T07:13:26Z","abstract_excerpt":"Natural language provides an intuitive and expressive way of conveying human intent to robots. Prior works employed end-to-end methods for learning trajectory deformations from language corrections. However, such methods do not generalize to new initial trajectories or object configurations. This work presents ExTraCT, a modular framework for trajectory corrections using natural language that combines Large Language Models (LLMs) for natural language understanding and trajectory deformation functions. Given a scene, ExTraCT generates the trajectory modification features (scene-specific and sce"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.03701","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2401.03701/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2401.03701","created_at":"2026-07-05T07:31:12.827951+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.03701v1","created_at":"2026-07-05T07:31:12.827951+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.03701","created_at":"2026-07-05T07:31:12.827951+00:00"},{"alias_kind":"pith_short_12","alias_value":"HLWTCXRB6MT7","created_at":"2026-07-05T07:31:12.827951+00:00"},{"alias_kind":"pith_short_16","alias_value":"HLWTCXRB6MT75FEV","created_at":"2026-07-05T07:31:12.827951+00:00"},{"alias_kind":"pith_short_8","alias_value":"HLWTCXRB","created_at":"2026-07-05T07:31:12.827951+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.17260","citing_title":"OVITA: Open-Vocabulary Interpretable Trajectory Adaptations","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HLWTCXRB6MT75FEVTH2ETFGYEP","json":"https://pith.science/pith/HLWTCXRB6MT75FEVTH2ETFGYEP.json","graph_json":"https://pith.science/api/pith-number/HLWTCXRB6MT75FEVTH2ETFGYEP/graph.json","events_json":"https://pith.science/api/pith-number/HLWTCXRB6MT75FEVTH2ETFGYEP/events.json","paper":"https://pith.science/paper/HLWTCXRB"},"agent_actions":{"view_html":"https://pith.science/pith/HLWTCXRB6MT75FEVTH2ETFGYEP","download_json":"https://pith.science/pith/HLWTCXRB6MT75FEVTH2ETFGYEP.json","view_paper":"https://pith.science/paper/HLWTCXRB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.03701&json=true","fetch_graph":"https://pith.science/api/pith-number/HLWTCXRB6MT75FEVTH2ETFGYEP/graph.json","fetch_events":"https://pith.science/api/pith-number/HLWTCXRB6MT75FEVTH2ETFGYEP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HLWTCXRB6MT75FEVTH2ETFGYEP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HLWTCXRB6MT75FEVTH2ETFGYEP/action/storage_attestation","attest_author":"https://pith.science/pith/HLWTCXRB6MT75FEVTH2ETFGYEP/action/author_attestation","sign_citation":"https://pith.science/pith/HLWTCXRB6MT75FEVTH2ETFGYEP/action/citation_signature","submit_replication":"https://pith.science/pith/HLWTCXRB6MT75FEVTH2ETFGYEP/action/replication_record"}},"created_at":"2026-07-05T07:31:12.827951+00:00","updated_at":"2026-07-05T07:31:12.827951+00:00"}