{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:COGT5E7VGJSC6LE5TUHUMUQUWP","short_pith_number":"pith:COGT5E7V","schema_version":"1.0","canonical_sha256":"138d3e93f532642f2c9d9d0f465214b3c7da447099e7c16598094d7ce1f11519","source":{"kind":"arxiv","id":"2409.17435","version":2},"attestation_state":"computed","paper":{"title":"Active Vision Might Be All You Need: Exploring Active Vision in Bimanual Robotic Manipulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Andrew Lee, Dechen Gao, Ian Chuang, Iman Soltani, M-Mahdi Naddaf-Sh","submitted_at":"2024-09-26T00:05:36Z","abstract_excerpt":"Imitation learning has demonstrated significant potential in performing high-precision manipulation tasks using visual feedback. However, it is common practice in imitation learning for cameras to be fixed in place, resulting in issues like occlusion and limited field of view. Furthermore, cameras are often placed in broad, general locations, without an effective viewpoint specific to the robot's task. In this work, we investigate the utility of active vision (AV) for imitation learning and manipulation, in which, in addition to the manipulation policy, the robot learns an AV policy from human"},"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":"2409.17435","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-09-26T00:05:36Z","cross_cats_sorted":[],"title_canon_sha256":"177750e302e100aa8c6f28483289f63e443918f701baddb1024d515e38bc9c70","abstract_canon_sha256":"557f554f582866555485542109c56fde15b5b0fd1bf2f174b49acff2a5cf59d6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:26:40.025087Z","signature_b64":"Jje900OQ77lp362ZKSScNIHJmTJ6xK/6eFqoTmZRFV1b3wLfx7izXZmu4j4IF/GaLS2lnhZKOSCjGD9ScPc8BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"138d3e93f532642f2c9d9d0f465214b3c7da447099e7c16598094d7ce1f11519","last_reissued_at":"2026-07-05T10:26:40.024193Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:26:40.024193Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Active Vision Might Be All You Need: Exploring Active Vision in Bimanual Robotic Manipulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Andrew Lee, Dechen Gao, Ian Chuang, Iman Soltani, M-Mahdi Naddaf-Sh","submitted_at":"2024-09-26T00:05:36Z","abstract_excerpt":"Imitation learning has demonstrated significant potential in performing high-precision manipulation tasks using visual feedback. However, it is common practice in imitation learning for cameras to be fixed in place, resulting in issues like occlusion and limited field of view. Furthermore, cameras are often placed in broad, general locations, without an effective viewpoint specific to the robot's task. In this work, we investigate the utility of active vision (AV) for imitation learning and manipulation, in which, in addition to the manipulation policy, the robot learns an AV policy from human"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.17435","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/2409.17435/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":"2409.17435","created_at":"2026-07-05T10:26:40.024338+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.17435v2","created_at":"2026-07-05T10:26:40.024338+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.17435","created_at":"2026-07-05T10:26:40.024338+00:00"},{"alias_kind":"pith_short_12","alias_value":"COGT5E7VGJSC","created_at":"2026-07-05T10:26:40.024338+00:00"},{"alias_kind":"pith_short_16","alias_value":"COGT5E7VGJSC6LE5","created_at":"2026-07-05T10:26:40.024338+00:00"},{"alias_kind":"pith_short_8","alias_value":"COGT5E7V","created_at":"2026-07-05T10:26:40.024338+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.10809","citing_title":"WARPED: Wrist-Aligned Rendering for Robot Policy Learning from Egocentric Human Demonstrations","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/COGT5E7VGJSC6LE5TUHUMUQUWP","json":"https://pith.science/pith/COGT5E7VGJSC6LE5TUHUMUQUWP.json","graph_json":"https://pith.science/api/pith-number/COGT5E7VGJSC6LE5TUHUMUQUWP/graph.json","events_json":"https://pith.science/api/pith-number/COGT5E7VGJSC6LE5TUHUMUQUWP/events.json","paper":"https://pith.science/paper/COGT5E7V"},"agent_actions":{"view_html":"https://pith.science/pith/COGT5E7VGJSC6LE5TUHUMUQUWP","download_json":"https://pith.science/pith/COGT5E7VGJSC6LE5TUHUMUQUWP.json","view_paper":"https://pith.science/paper/COGT5E7V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.17435&json=true","fetch_graph":"https://pith.science/api/pith-number/COGT5E7VGJSC6LE5TUHUMUQUWP/graph.json","fetch_events":"https://pith.science/api/pith-number/COGT5E7VGJSC6LE5TUHUMUQUWP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/COGT5E7VGJSC6LE5TUHUMUQUWP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/COGT5E7VGJSC6LE5TUHUMUQUWP/action/storage_attestation","attest_author":"https://pith.science/pith/COGT5E7VGJSC6LE5TUHUMUQUWP/action/author_attestation","sign_citation":"https://pith.science/pith/COGT5E7VGJSC6LE5TUHUMUQUWP/action/citation_signature","submit_replication":"https://pith.science/pith/COGT5E7VGJSC6LE5TUHUMUQUWP/action/replication_record"}},"created_at":"2026-07-05T10:26:40.024338+00:00","updated_at":"2026-07-05T10:26:40.024338+00:00"}