{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RZORXUBST5NTAUMHGZZ3SKI3XQ","short_pith_number":"pith:RZORXUBS","schema_version":"1.0","canonical_sha256":"8e5d1bd0329f5b3051873673b9291bbc1cfdcc00d4f7b8f7762bb3f4e15a0970","source":{"kind":"arxiv","id":"2606.24403","version":1},"attestation_state":"computed","paper":{"title":"RE4: Transformation-aware Imitation of Object Interactions Using Manipulation Modes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Arsh Chawla, Rahul Shome","submitted_at":"2026-06-23T10:37:50Z","abstract_excerpt":"Object interaction tasks have been a focus of advances in imitation learning. End-to-end methods, dominated by diffusion and flow-based variants have shown leaps in performance while sacrificing interpretability. Object-centric and pose-informed variants have had a role in learning from demonstration in manipulation tasks. In this paper, we revisit a few modern imitation learning benchmarks for object interactions, with the aim of composing a framework that repurposes principled theories of manipulation, preserving both performance and interpretability. For image observations, lightweight trai"},"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":"2606.24403","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-06-23T10:37:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5f0413736d8ab3ab087680131d50e687da71b51e199f2a569ecd0df1aa0b0209","abstract_canon_sha256":"5f211092966792811eb004c290aee12b123f5811b89871102bb29b7a835133c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T01:15:29.631462Z","signature_b64":"G4n0plCs2vPEXkemOhm/6rL7u1/xHODKB6vLrreiHqazBSr7C7CPyid8MICz9+uQ49iPD6pN4LjLgHsfsVi9BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e5d1bd0329f5b3051873673b9291bbc1cfdcc00d4f7b8f7762bb3f4e15a0970","last_reissued_at":"2026-06-24T01:15:29.631106Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T01:15:29.631106Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RE4: Transformation-aware Imitation of Object Interactions Using Manipulation Modes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Arsh Chawla, Rahul Shome","submitted_at":"2026-06-23T10:37:50Z","abstract_excerpt":"Object interaction tasks have been a focus of advances in imitation learning. End-to-end methods, dominated by diffusion and flow-based variants have shown leaps in performance while sacrificing interpretability. Object-centric and pose-informed variants have had a role in learning from demonstration in manipulation tasks. In this paper, we revisit a few modern imitation learning benchmarks for object interactions, with the aim of composing a framework that repurposes principled theories of manipulation, preserving both performance and interpretability. For image observations, lightweight trai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.24403","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/2606.24403/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":"2606.24403","created_at":"2026-06-24T01:15:29.631166+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.24403v1","created_at":"2026-06-24T01:15:29.631166+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.24403","created_at":"2026-06-24T01:15:29.631166+00:00"},{"alias_kind":"pith_short_12","alias_value":"RZORXUBST5NT","created_at":"2026-06-24T01:15:29.631166+00:00"},{"alias_kind":"pith_short_16","alias_value":"RZORXUBST5NTAUMH","created_at":"2026-06-24T01:15:29.631166+00:00"},{"alias_kind":"pith_short_8","alias_value":"RZORXUBS","created_at":"2026-06-24T01:15:29.631166+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/RZORXUBST5NTAUMHGZZ3SKI3XQ","json":"https://pith.science/pith/RZORXUBST5NTAUMHGZZ3SKI3XQ.json","graph_json":"https://pith.science/api/pith-number/RZORXUBST5NTAUMHGZZ3SKI3XQ/graph.json","events_json":"https://pith.science/api/pith-number/RZORXUBST5NTAUMHGZZ3SKI3XQ/events.json","paper":"https://pith.science/paper/RZORXUBS"},"agent_actions":{"view_html":"https://pith.science/pith/RZORXUBST5NTAUMHGZZ3SKI3XQ","download_json":"https://pith.science/pith/RZORXUBST5NTAUMHGZZ3SKI3XQ.json","view_paper":"https://pith.science/paper/RZORXUBS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.24403&json=true","fetch_graph":"https://pith.science/api/pith-number/RZORXUBST5NTAUMHGZZ3SKI3XQ/graph.json","fetch_events":"https://pith.science/api/pith-number/RZORXUBST5NTAUMHGZZ3SKI3XQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RZORXUBST5NTAUMHGZZ3SKI3XQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RZORXUBST5NTAUMHGZZ3SKI3XQ/action/storage_attestation","attest_author":"https://pith.science/pith/RZORXUBST5NTAUMHGZZ3SKI3XQ/action/author_attestation","sign_citation":"https://pith.science/pith/RZORXUBST5NTAUMHGZZ3SKI3XQ/action/citation_signature","submit_replication":"https://pith.science/pith/RZORXUBST5NTAUMHGZZ3SKI3XQ/action/replication_record"}},"created_at":"2026-06-24T01:15:29.631166+00:00","updated_at":"2026-06-24T01:15:29.631166+00:00"}