{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:232FBBD6VM74ZEXFMQ7THYPUXV","short_pith_number":"pith:232FBBD6","schema_version":"1.0","canonical_sha256":"d6f450847eab3fcc92e5643f33e1f4bd43e676526743ad454579dedc5969b3f5","source":{"kind":"arxiv","id":"2606.21600","version":1},"attestation_state":"computed","paper":{"title":"VQActFlow: Vector-Quantized Action Mode Steering for Multi-Task Robot Manipulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Haoran Liu, Huishu Xue, Mark Leggiero, Sirui Zhan, Ye Zhao, Yifan Wu, Yipu Chen, Zhigen Zhao","submitted_at":"2026-06-19T16:56:54Z","abstract_excerpt":"Multi-task robot manipulation policies are challenging to learn from demonstration because traditionally a single network must select among qualitatively different action modes from a multimodal demonstration distribution, conditioned on language and visual context. A wrong mode selection means executing the wrong task or an action infeasible in the scene. Tokenizing continuous actions into a learned discrete codebook separates these modes at the representation level, offering structural advantages for multi-task learning. We propose VQActFlow, a multi-task manipulation policy that tokenizes a"},"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":"2606.21600","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-06-19T16:56:54Z","cross_cats_sorted":[],"title_canon_sha256":"3fb863ef132f41a68a01fe870f466eaca0da6bde47b484612bd8d62544a0e10c","abstract_canon_sha256":"da3cb9f8689b3b63e7c3cf53c9f61f3854e6475b4b9bcd72ac555abfe897e393"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T01:13:15.900100Z","signature_b64":"YTD99tY8bpe/IItGu67jgW+7oNOnXG4HIP51jyrA2MPcCbZdoucHTcaTtHpzhdSv0EEc/RPybhmDv7kHtP31Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6f450847eab3fcc92e5643f33e1f4bd43e676526743ad454579dedc5969b3f5","last_reissued_at":"2026-06-23T01:13:15.899695Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T01:13:15.899695Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VQActFlow: Vector-Quantized Action Mode Steering for Multi-Task Robot Manipulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Haoran Liu, Huishu Xue, Mark Leggiero, Sirui Zhan, Ye Zhao, Yifan Wu, Yipu Chen, Zhigen Zhao","submitted_at":"2026-06-19T16:56:54Z","abstract_excerpt":"Multi-task robot manipulation policies are challenging to learn from demonstration because traditionally a single network must select among qualitatively different action modes from a multimodal demonstration distribution, conditioned on language and visual context. A wrong mode selection means executing the wrong task or an action infeasible in the scene. Tokenizing continuous actions into a learned discrete codebook separates these modes at the representation level, offering structural advantages for multi-task learning. We propose VQActFlow, a multi-task manipulation policy that tokenizes a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.21600","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/2606.21600/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":"2606.21600","created_at":"2026-06-23T01:13:15.899759+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.21600v1","created_at":"2026-06-23T01:13:15.899759+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.21600","created_at":"2026-06-23T01:13:15.899759+00:00"},{"alias_kind":"pith_short_12","alias_value":"232FBBD6VM74","created_at":"2026-06-23T01:13:15.899759+00:00"},{"alias_kind":"pith_short_16","alias_value":"232FBBD6VM74ZEXF","created_at":"2026-06-23T01:13:15.899759+00:00"},{"alias_kind":"pith_short_8","alias_value":"232FBBD6","created_at":"2026-06-23T01:13:15.899759+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/232FBBD6VM74ZEXFMQ7THYPUXV","json":"https://pith.science/pith/232FBBD6VM74ZEXFMQ7THYPUXV.json","graph_json":"https://pith.science/api/pith-number/232FBBD6VM74ZEXFMQ7THYPUXV/graph.json","events_json":"https://pith.science/api/pith-number/232FBBD6VM74ZEXFMQ7THYPUXV/events.json","paper":"https://pith.science/paper/232FBBD6"},"agent_actions":{"view_html":"https://pith.science/pith/232FBBD6VM74ZEXFMQ7THYPUXV","download_json":"https://pith.science/pith/232FBBD6VM74ZEXFMQ7THYPUXV.json","view_paper":"https://pith.science/paper/232FBBD6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.21600&json=true","fetch_graph":"https://pith.science/api/pith-number/232FBBD6VM74ZEXFMQ7THYPUXV/graph.json","fetch_events":"https://pith.science/api/pith-number/232FBBD6VM74ZEXFMQ7THYPUXV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/232FBBD6VM74ZEXFMQ7THYPUXV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/232FBBD6VM74ZEXFMQ7THYPUXV/action/storage_attestation","attest_author":"https://pith.science/pith/232FBBD6VM74ZEXFMQ7THYPUXV/action/author_attestation","sign_citation":"https://pith.science/pith/232FBBD6VM74ZEXFMQ7THYPUXV/action/citation_signature","submit_replication":"https://pith.science/pith/232FBBD6VM74ZEXFMQ7THYPUXV/action/replication_record"}},"created_at":"2026-06-23T01:13:15.899759+00:00","updated_at":"2026-06-23T01:13:15.899759+00:00"}