{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:A2P5MBQTTMXH2XVLBSAHZHYFJN","short_pith_number":"pith:A2P5MBQT","schema_version":"1.0","canonical_sha256":"069fd606139b2e7d5eab0c807c9f054b4c7609e2f3b2d2e1d84fe896d4fec73d","source":{"kind":"arxiv","id":"2208.02918","version":3},"attestation_state":"computed","paper":{"title":"LATTE: LAnguage Trajectory TransformEr","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Arthur Bucker, Ashish Kapoor, Luis Figueredo, Rogerio Bonatti, Sai Vemprala, Sami Haddadin, Shuang Ma","submitted_at":"2022-08-04T22:43:21Z","abstract_excerpt":"Natural language is one of the most intuitive ways to express human intent. However, translating instructions and commands towards robotic motion generation and deployment in the real world is far from being an easy task. The challenge of combining a robot's inherent low-level geometric and kinodynamic constraints with a human's high-level semantic instructions traditionally is solved using task-specific solutions with little generalizability between hardware platforms, often with the use of static sets of target actions and commands. This work instead proposes a flexible language-based framew"},"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":"2208.02918","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2022-08-04T22:43:21Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CV","cs.LG"],"title_canon_sha256":"bd7202cdb23c4a460a65fdbb654d5671a1678db1bfdf0356e5675d00d0099c57","abstract_canon_sha256":"4de0c38bdac58beb4154cf29b17ba5de7f6f7bae10ffa75a4e812a40e9d3739b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:58:16.725617Z","signature_b64":"JbVYZ9/s+9VO2UeyA2FmVKPGgldYEmtbLiyllhTT38WeTOoi4s8lsat/H3EyWatZRor8EP1mBC41qd9iMRc2Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"069fd606139b2e7d5eab0c807c9f054b4c7609e2f3b2d2e1d84fe896d4fec73d","last_reissued_at":"2026-07-05T04:58:16.725098Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:58:16.725098Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LATTE: LAnguage Trajectory TransformEr","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Arthur Bucker, Ashish Kapoor, Luis Figueredo, Rogerio Bonatti, Sai Vemprala, Sami Haddadin, Shuang Ma","submitted_at":"2022-08-04T22:43:21Z","abstract_excerpt":"Natural language is one of the most intuitive ways to express human intent. However, translating instructions and commands towards robotic motion generation and deployment in the real world is far from being an easy task. The challenge of combining a robot's inherent low-level geometric and kinodynamic constraints with a human's high-level semantic instructions traditionally is solved using task-specific solutions with little generalizability between hardware platforms, often with the use of static sets of target actions and commands. This work instead proposes a flexible language-based framew"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.02918","kind":"arxiv","version":3},"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/2208.02918/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":"2208.02918","created_at":"2026-07-05T04:58:16.725156+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.02918v3","created_at":"2026-07-05T04:58:16.725156+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.02918","created_at":"2026-07-05T04:58:16.725156+00:00"},{"alias_kind":"pith_short_12","alias_value":"A2P5MBQTTMXH","created_at":"2026-07-05T04:58:16.725156+00:00"},{"alias_kind":"pith_short_16","alias_value":"A2P5MBQTTMXH2XVL","created_at":"2026-07-05T04:58:16.725156+00:00"},{"alias_kind":"pith_short_8","alias_value":"A2P5MBQT","created_at":"2026-07-05T04:58:16.725156+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.07034","citing_title":"KITE: Keyframe-Indexed Tokenized Evidence for VLM-Based Robot Failure Analysis","ref_index":34,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/A2P5MBQTTMXH2XVLBSAHZHYFJN","json":"https://pith.science/pith/A2P5MBQTTMXH2XVLBSAHZHYFJN.json","graph_json":"https://pith.science/api/pith-number/A2P5MBQTTMXH2XVLBSAHZHYFJN/graph.json","events_json":"https://pith.science/api/pith-number/A2P5MBQTTMXH2XVLBSAHZHYFJN/events.json","paper":"https://pith.science/paper/A2P5MBQT"},"agent_actions":{"view_html":"https://pith.science/pith/A2P5MBQTTMXH2XVLBSAHZHYFJN","download_json":"https://pith.science/pith/A2P5MBQTTMXH2XVLBSAHZHYFJN.json","view_paper":"https://pith.science/paper/A2P5MBQT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.02918&json=true","fetch_graph":"https://pith.science/api/pith-number/A2P5MBQTTMXH2XVLBSAHZHYFJN/graph.json","fetch_events":"https://pith.science/api/pith-number/A2P5MBQTTMXH2XVLBSAHZHYFJN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A2P5MBQTTMXH2XVLBSAHZHYFJN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A2P5MBQTTMXH2XVLBSAHZHYFJN/action/storage_attestation","attest_author":"https://pith.science/pith/A2P5MBQTTMXH2XVLBSAHZHYFJN/action/author_attestation","sign_citation":"https://pith.science/pith/A2P5MBQTTMXH2XVLBSAHZHYFJN/action/citation_signature","submit_replication":"https://pith.science/pith/A2P5MBQTTMXH2XVLBSAHZHYFJN/action/replication_record"}},"created_at":"2026-07-05T04:58:16.725156+00:00","updated_at":"2026-07-05T04:58:16.725156+00:00"}