{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:TR3RQEMAR2UYGVKTGCIJD5UCES","short_pith_number":"pith:TR3RQEMA","schema_version":"1.0","canonical_sha256":"9c771811808ea9835553309091f68224ae9ed11a3f21506582b9d291eb76f367","source":{"kind":"arxiv","id":"2310.06226","version":1},"attestation_state":"computed","paper":{"title":"Words into Action: Learning Diverse Humanoid Robot Behaviors using Language Guided Iterative Motion Refinement","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Irfan Essa, K. Niranjan Kumar, Sehoon Ha","submitted_at":"2023-10-10T00:39:37Z","abstract_excerpt":"Humanoid robots are well suited for human habitats due to their morphological similarity, but developing controllers for them is a challenging task that involves multiple sub-problems, such as control, planning and perception. In this paper, we introduce a method to simplify controller design by enabling users to train and fine-tune robot control policies using natural language commands. We first learn a neural network policy that generates behaviors given a natural language command, such as \"walk forward\", by combining Large Language Models (LLMs), motion retargeting, and motion imitation. Ba"},"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":"2310.06226","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2023-10-10T00:39:37Z","cross_cats_sorted":[],"title_canon_sha256":"3fcaa0870a67239acabfe39f3ad8caf8e6fa3090036bc7bc9db28362612af064","abstract_canon_sha256":"b5723c7575da7e5b6797e52880ee0a17125f57a66147543a442eada84120abf5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:59:07.316061Z","signature_b64":"l4ecD/YpTbRfXuKTZV7/Dm/qvD9boBT2ct2KjlW2YWUEPOGj2KW0XyuQB+aChR5kgNRCkqW8T24co65qMV2qBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c771811808ea9835553309091f68224ae9ed11a3f21506582b9d291eb76f367","last_reissued_at":"2026-07-05T06:59:07.315498Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:59:07.315498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Words into Action: Learning Diverse Humanoid Robot Behaviors using Language Guided Iterative Motion Refinement","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Irfan Essa, K. Niranjan Kumar, Sehoon Ha","submitted_at":"2023-10-10T00:39:37Z","abstract_excerpt":"Humanoid robots are well suited for human habitats due to their morphological similarity, but developing controllers for them is a challenging task that involves multiple sub-problems, such as control, planning and perception. In this paper, we introduce a method to simplify controller design by enabling users to train and fine-tune robot control policies using natural language commands. We first learn a neural network policy that generates behaviors given a natural language command, such as \"walk forward\", by combining Large Language Models (LLMs), motion retargeting, and motion imitation. Ba"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.06226","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/2310.06226/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":"2310.06226","created_at":"2026-07-05T06:59:07.315565+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.06226v1","created_at":"2026-07-05T06:59:07.315565+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.06226","created_at":"2026-07-05T06:59:07.315565+00:00"},{"alias_kind":"pith_short_12","alias_value":"TR3RQEMAR2UY","created_at":"2026-07-05T06:59:07.315565+00:00"},{"alias_kind":"pith_short_16","alias_value":"TR3RQEMAR2UYGVKT","created_at":"2026-07-05T06:59:07.315565+00:00"},{"alias_kind":"pith_short_8","alias_value":"TR3RQEMA","created_at":"2026-07-05T06:59:07.315565+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.07765","citing_title":"Toward Seamless Physical Human-Humanoid Interaction: Insights from Control, Intent, and Modeling with a Vision for What Comes Next","ref_index":115,"is_internal_anchor":false},{"citing_arxiv_id":"2401.03568","citing_title":"Agent AI: Surveying the Horizons of Multimodal Interaction","ref_index":152,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TR3RQEMAR2UYGVKTGCIJD5UCES","json":"https://pith.science/pith/TR3RQEMAR2UYGVKTGCIJD5UCES.json","graph_json":"https://pith.science/api/pith-number/TR3RQEMAR2UYGVKTGCIJD5UCES/graph.json","events_json":"https://pith.science/api/pith-number/TR3RQEMAR2UYGVKTGCIJD5UCES/events.json","paper":"https://pith.science/paper/TR3RQEMA"},"agent_actions":{"view_html":"https://pith.science/pith/TR3RQEMAR2UYGVKTGCIJD5UCES","download_json":"https://pith.science/pith/TR3RQEMAR2UYGVKTGCIJD5UCES.json","view_paper":"https://pith.science/paper/TR3RQEMA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.06226&json=true","fetch_graph":"https://pith.science/api/pith-number/TR3RQEMAR2UYGVKTGCIJD5UCES/graph.json","fetch_events":"https://pith.science/api/pith-number/TR3RQEMAR2UYGVKTGCIJD5UCES/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TR3RQEMAR2UYGVKTGCIJD5UCES/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TR3RQEMAR2UYGVKTGCIJD5UCES/action/storage_attestation","attest_author":"https://pith.science/pith/TR3RQEMAR2UYGVKTGCIJD5UCES/action/author_attestation","sign_citation":"https://pith.science/pith/TR3RQEMAR2UYGVKTGCIJD5UCES/action/citation_signature","submit_replication":"https://pith.science/pith/TR3RQEMAR2UYGVKTGCIJD5UCES/action/replication_record"}},"created_at":"2026-07-05T06:59:07.315565+00:00","updated_at":"2026-07-05T06:59:07.315565+00:00"}