{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QGG6NCBFDQNMQNHQ6ZLSAHCTBX","short_pith_number":"pith:QGG6NCBF","schema_version":"1.0","canonical_sha256":"818de688251c1ac834f0f657201c530dda5a8b68ad2edc00774a5919d7b19158","source":{"kind":"arxiv","id":"2409.02636","version":2},"attestation_state":"computed","paper":{"title":"Mamba as a motion encoder for robotic imitation learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Toshiaki Tsuji","submitted_at":"2024-09-04T11:59:53Z","abstract_excerpt":"Recent advancements in imitation learning, particularly with the integration of LLM techniques, are set to significantly improve robots' dexterity and adaptability. This paper proposes using Mamba, a state-of-the-art architecture with potential applications in LLMs, for robotic imitation learning, highlighting its ability to function as an encoder that effectively captures contextual information. By reducing the dimensionality of the state space, Mamba operates similarly to an autoencoder. It effectively compresses the sequential information into state variables while preserving the essential "},"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":"2409.02636","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-09-04T11:59:53Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"12d7ff519cd9dda45eca9d6d9e84ecac1da730874fe05eff90ec53d622ff6ff7","abstract_canon_sha256":"e3204b14f27de26bb75fa64f00a323ca6fffc854b9c9f735c396874b062de618"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:11:29.410875Z","signature_b64":"mh7eeUUqK6O04ZGXekaB2m1vTR+N1xqXhvuLFPCWWfbm3kAUaGJCzMQkW5g+7IS1uZhffz4B1QZhoknbD4ykAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"818de688251c1ac834f0f657201c530dda5a8b68ad2edc00774a5919d7b19158","last_reissued_at":"2026-07-05T09:11:29.410407Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:11:29.410407Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mamba as a motion encoder for robotic imitation learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Toshiaki Tsuji","submitted_at":"2024-09-04T11:59:53Z","abstract_excerpt":"Recent advancements in imitation learning, particularly with the integration of LLM techniques, are set to significantly improve robots' dexterity and adaptability. This paper proposes using Mamba, a state-of-the-art architecture with potential applications in LLMs, for robotic imitation learning, highlighting its ability to function as an encoder that effectively captures contextual information. By reducing the dimensionality of the state space, Mamba operates similarly to an autoencoder. It effectively compresses the sequential information into state variables while preserving the essential "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.02636","kind":"arxiv","version":2},"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/2409.02636/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":"2409.02636","created_at":"2026-07-05T09:11:29.410468+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.02636v2","created_at":"2026-07-05T09:11:29.410468+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.02636","created_at":"2026-07-05T09:11:29.410468+00:00"},{"alias_kind":"pith_short_12","alias_value":"QGG6NCBFDQNM","created_at":"2026-07-05T09:11:29.410468+00:00"},{"alias_kind":"pith_short_16","alias_value":"QGG6NCBFDQNMQNHQ","created_at":"2026-07-05T09:11:29.410468+00:00"},{"alias_kind":"pith_short_8","alias_value":"QGG6NCBF","created_at":"2026-07-05T09:11:29.410468+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.09942","citing_title":"ALPHA-$\\alpha$ and Bi-ACT Are All You Need: Importance of Position and Force Information/Control for Imitation Learning of Unimanual and Bimanual Robotic Manipulation with Low-Cost System","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QGG6NCBFDQNMQNHQ6ZLSAHCTBX","json":"https://pith.science/pith/QGG6NCBFDQNMQNHQ6ZLSAHCTBX.json","graph_json":"https://pith.science/api/pith-number/QGG6NCBFDQNMQNHQ6ZLSAHCTBX/graph.json","events_json":"https://pith.science/api/pith-number/QGG6NCBFDQNMQNHQ6ZLSAHCTBX/events.json","paper":"https://pith.science/paper/QGG6NCBF"},"agent_actions":{"view_html":"https://pith.science/pith/QGG6NCBFDQNMQNHQ6ZLSAHCTBX","download_json":"https://pith.science/pith/QGG6NCBFDQNMQNHQ6ZLSAHCTBX.json","view_paper":"https://pith.science/paper/QGG6NCBF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.02636&json=true","fetch_graph":"https://pith.science/api/pith-number/QGG6NCBFDQNMQNHQ6ZLSAHCTBX/graph.json","fetch_events":"https://pith.science/api/pith-number/QGG6NCBFDQNMQNHQ6ZLSAHCTBX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QGG6NCBFDQNMQNHQ6ZLSAHCTBX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QGG6NCBFDQNMQNHQ6ZLSAHCTBX/action/storage_attestation","attest_author":"https://pith.science/pith/QGG6NCBFDQNMQNHQ6ZLSAHCTBX/action/author_attestation","sign_citation":"https://pith.science/pith/QGG6NCBFDQNMQNHQ6ZLSAHCTBX/action/citation_signature","submit_replication":"https://pith.science/pith/QGG6NCBFDQNMQNHQ6ZLSAHCTBX/action/replication_record"}},"created_at":"2026-07-05T09:11:29.410468+00:00","updated_at":"2026-07-05T09:11:29.410468+00:00"}