{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:XTYUDGOZ4Y3CACLKJREWES4ZI4","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"59ac72354d0f28df366edfe84cf6a6214012436fce33b56d9d2a7820cf005497","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-02-24T19:13:25Z","title_canon_sha256":"ab111a331b479784dd3af2f490c6a4753edb0978f18a744b9a85e783a2cb8663"},"schema_version":"1.0","source":{"id":"2602.21302","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.21302","created_at":"2026-05-17T23:39:15Z"},{"alias_kind":"arxiv_version","alias_value":"2602.21302v2","created_at":"2026-05-17T23:39:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.21302","created_at":"2026-05-17T23:39:15Z"},{"alias_kind":"pith_short_12","alias_value":"XTYUDGOZ4Y3C","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"XTYUDGOZ4Y3CACLK","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"XTYUDGOZ","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:f406139723b88ac3f0a45fcf1586236bdc47241acfe6f7bde05066250a17f0af","target":"graph","created_at":"2026-05-17T23:39:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Using a single human demonstration and a simplified rope model, the method learns directly on hardware without reliance on large amounts of demonstration data or massive amounts of simulation. Learning achieves a 100% success rate within 10 trials on all ropes. Furthermore, the method can successfully transfer between most rope types in 2--5 trials."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The simplified rope model is sufficient to allow quadratic program inversion to accurately propagate task-space errors into effective action updates for the dynamic flying knot task."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Task-level ILC learns flying knot rope manipulation from one demo, achieving 100% success within 10 trials on 7 rope types with 2-5 trial transfers."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Task-level iterative learning control lets robots master dynamic flying knots from one human demonstration and a simplified rope model."}],"snapshot_sha256":"8ef27b1eb34055f61f337555bfedd9a46f8f83863909c0bdc7acc444161c1801"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"0c73b4abab8cd1f17cfb7335c926ff8e35dbf29f1d4b35516d6540020d5d2fea"},"paper":{"abstract_excerpt":"We introduce a Task-Level Iterative Learning Control method for dynamic manipulation of ropes. We demonstrate this method on a non-planar rope manipulation task called the flying knot. Using a single human demonstration and a simplified rope model, the method learns directly on hardware without reliance on large amounts of demonstration data or massive amounts of simulation. At each iteration, the algorithm inverts a model of the robot and rope by solving a quadratic program to propagate task-space errors into action updates. We evaluate performance across 7 different kinds of ropes, including","authors_text":"Chris Atkeson, Krishna Suresh","cross_cats":[],"headline":"Task-level iterative learning control lets robots master dynamic flying knots from one human demonstration and a simplified rope model.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-02-24T19:13:25Z","title":"Learning Dynamic Rope Manipulation Using Task-Level Iterative Learning Control"},"references":{"count":51,"internal_anchors":0,"resolved_work":51,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Using inaccurate models in reinforcement learning","work_id":"218aa5d7-38f3-4e8d-a9bb-1ff705c31c6e","year":2006},{"cited_arxiv_id":"","doi":"10.1109/robot","is_internal_anchor":false,"ref_index":2,"title":"The enhanced compact qp method for redundant manipulators using practical inequality constraints","work_id":"d203a59f-a6de-4624-8a99-b6b970c2bd77","year":1988},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"A regret minimization approach to iterative learning control","work_id":"fff1819c-3545-40fa-9722-528824638926","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"URL https://proceedings.mlr.press/v139/ agarwal21b.html","work_id":"f17c1ef8-a029-4acb-a153-19c1e93eeac7","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Chae H. An, Christopher G. Atkeson, and John Holler- bach.Model-Based Control of a Robot Manipulator. Artificial Intelligence Series. MIT Press. ISBN 978-0- 262-51157-5","work_id":"762f56f6-92df-4011-a334-af66a1aa8a7e","year":null}],"snapshot_sha256":"b570041887e374b3e3550647299eeb8c3cb9ba75904d9c27a4c7f4b3247cb144"},"source":{"id":"2602.21302","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-15T19:41:16.455300Z","id":"87f6c342-73f1-437d-b4f4-ac6c90e479f5","model_set":{"reader":"grok-4.3"},"one_line_summary":"Task-level ILC learns flying knot rope manipulation from one demo, achieving 100% success within 10 trials on 7 rope types with 2-5 trial transfers.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Task-level iterative learning control lets robots master dynamic flying knots from one human demonstration and a simplified rope model.","strongest_claim":"Using a single human demonstration and a simplified rope model, the method learns directly on hardware without reliance on large amounts of demonstration data or massive amounts of simulation. Learning achieves a 100% success rate within 10 trials on all ropes. Furthermore, the method can successfully transfer between most rope types in 2--5 trials.","weakest_assumption":"The simplified rope model is sufficient to allow quadratic program inversion to accurately propagate task-space errors into effective action updates for the dynamic flying knot task."}},"verdict_id":"87f6c342-73f1-437d-b4f4-ac6c90e479f5"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9d3c8cd6985f14ba80fa2d106b0502aecc11b91059c3d2e363d108be10c5dda3","target":"record","created_at":"2026-05-17T23:39:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"59ac72354d0f28df366edfe84cf6a6214012436fce33b56d9d2a7820cf005497","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-02-24T19:13:25Z","title_canon_sha256":"ab111a331b479784dd3af2f490c6a4753edb0978f18a744b9a85e783a2cb8663"},"schema_version":"1.0","source":{"id":"2602.21302","kind":"arxiv","version":2}},"canonical_sha256":"bcf14199d9e63620096a4c49624b99473bb6173776c375f48b1faede1a3d7ec6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bcf14199d9e63620096a4c49624b99473bb6173776c375f48b1faede1a3d7ec6","first_computed_at":"2026-05-17T23:39:15.978499Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:15.978499Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8q9nXShznit+rgkozTh7V486HnlJuw2YVx9rnW0lke7XaYU5JmPDM5po1WmpZBZ0v6860Ku+XX4ddM++sMnQBQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:15.979197Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.21302","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9d3c8cd6985f14ba80fa2d106b0502aecc11b91059c3d2e363d108be10c5dda3","sha256:f406139723b88ac3f0a45fcf1586236bdc47241acfe6f7bde05066250a17f0af"],"state_sha256":"36a2c7444494b7a289fe2ebf61d57f24892b2cc28e46caf08c597074ac09d851"}