{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:BR2OOWVDM44WZKTFHVJYP4IFHW","short_pith_number":"pith:BR2OOWVD","schema_version":"1.0","canonical_sha256":"0c74e75aa367396caa653d5387f1053db3dfcd4c7cf9d2d4b755d208922f7891","source":{"kind":"arxiv","id":"2003.01835","version":1},"attestation_state":"computed","paper":{"title":"Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Ashwin Balakrishna, Brijen Thananjeyan, Jennifer Grannen, Joseph E. Gonzalez, Ken Goldberg, Kevin Stone, Michael Laskey, Priya Sundaresan","submitted_at":"2020-03-03T23:43:05Z","abstract_excerpt":"Robotic manipulation of deformable 1D objects such as ropes, cables, and hoses is challenging due to the lack of high-fidelity analytic models and large configuration spaces. Furthermore, learning end-to-end manipulation policies directly from images and physical interaction requires significant time on a robot and can fail to generalize across tasks. We address these challenges using interpretable deep visual representations for rope, extending recent work on dense object descriptors for robot manipulation. This facilitates the design of interpretable and transferable geometric policies built"},"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":"2003.01835","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-03-03T23:43:05Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"d9b7833966f8c93c1331fd1254f97ca016de7da588f887500528e5efdd67b43b","abstract_canon_sha256":"2589518f865c60fc08495329da8e3f8eb34b9369fb195219a41bc13d9606ae21"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:45:45.277876Z","signature_b64":"q8s16clHMBoCv0pNVJJxQPpCl/z3TaWq4KaSqKPCXwjVO08pfvYNINEWexnogpFJy9lRkh9j1jbDa4QJ1AsDDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c74e75aa367396caa653d5387f1053db3dfcd4c7cf9d2d4b755d208922f7891","last_reissued_at":"2026-07-05T00:45:45.277450Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:45:45.277450Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Ashwin Balakrishna, Brijen Thananjeyan, Jennifer Grannen, Joseph E. Gonzalez, Ken Goldberg, Kevin Stone, Michael Laskey, Priya Sundaresan","submitted_at":"2020-03-03T23:43:05Z","abstract_excerpt":"Robotic manipulation of deformable 1D objects such as ropes, cables, and hoses is challenging due to the lack of high-fidelity analytic models and large configuration spaces. Furthermore, learning end-to-end manipulation policies directly from images and physical interaction requires significant time on a robot and can fail to generalize across tasks. We address these challenges using interpretable deep visual representations for rope, extending recent work on dense object descriptors for robot manipulation. This facilitates the design of interpretable and transferable geometric policies built"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.01835","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/2003.01835/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":"2003.01835","created_at":"2026-07-05T00:45:45.277506+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.01835v1","created_at":"2026-07-05T00:45:45.277506+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.01835","created_at":"2026-07-05T00:45:45.277506+00:00"},{"alias_kind":"pith_short_12","alias_value":"BR2OOWVDM44W","created_at":"2026-07-05T00:45:45.277506+00:00"},{"alias_kind":"pith_short_16","alias_value":"BR2OOWVDM44WZKTF","created_at":"2026-07-05T00:45:45.277506+00:00"},{"alias_kind":"pith_short_8","alias_value":"BR2OOWVD","created_at":"2026-07-05T00:45:45.277506+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.03889","citing_title":"Reactive In-Air Clothing Manipulation with Confidence-Aware Dense Correspondence and Visuotactile Affordance","ref_index":27,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BR2OOWVDM44WZKTFHVJYP4IFHW","json":"https://pith.science/pith/BR2OOWVDM44WZKTFHVJYP4IFHW.json","graph_json":"https://pith.science/api/pith-number/BR2OOWVDM44WZKTFHVJYP4IFHW/graph.json","events_json":"https://pith.science/api/pith-number/BR2OOWVDM44WZKTFHVJYP4IFHW/events.json","paper":"https://pith.science/paper/BR2OOWVD"},"agent_actions":{"view_html":"https://pith.science/pith/BR2OOWVDM44WZKTFHVJYP4IFHW","download_json":"https://pith.science/pith/BR2OOWVDM44WZKTFHVJYP4IFHW.json","view_paper":"https://pith.science/paper/BR2OOWVD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.01835&json=true","fetch_graph":"https://pith.science/api/pith-number/BR2OOWVDM44WZKTFHVJYP4IFHW/graph.json","fetch_events":"https://pith.science/api/pith-number/BR2OOWVDM44WZKTFHVJYP4IFHW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BR2OOWVDM44WZKTFHVJYP4IFHW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BR2OOWVDM44WZKTFHVJYP4IFHW/action/storage_attestation","attest_author":"https://pith.science/pith/BR2OOWVDM44WZKTFHVJYP4IFHW/action/author_attestation","sign_citation":"https://pith.science/pith/BR2OOWVDM44WZKTFHVJYP4IFHW/action/citation_signature","submit_replication":"https://pith.science/pith/BR2OOWVDM44WZKTFHVJYP4IFHW/action/replication_record"}},"created_at":"2026-07-05T00:45:45.277506+00:00","updated_at":"2026-07-05T00:45:45.277506+00:00"}