{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:VVJTUATQQVZP6HKNB3O3P5Z7YQ","short_pith_number":"pith:VVJTUATQ","canonical_record":{"source":{"id":"1710.10122","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-10-27T13:24:35Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"c5497ca5c716594238321892673baf823f6feb8c110953ea66d57c65af11aae2","abstract_canon_sha256":"32d70f36061360f7ddd759e47ed27fb327740fbaa1b6534fdf248fb200261a20"},"schema_version":"1.0"},"canonical_sha256":"ad533a02708572ff1d4d0eddb7f73fc4064856ac3092aec82b48c09f6040b927","source":{"kind":"arxiv","id":"1710.10122","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.10122","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"arxiv_version","alias_value":"1710.10122v1","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.10122","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"pith_short_12","alias_value":"VVJTUATQQVZP","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VVJTUATQQVZP6HKN","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VVJTUATQ","created_at":"2026-05-18T12:31:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:VVJTUATQQVZP6HKNB3O3P5Z7YQ","target":"record","payload":{"canonical_record":{"source":{"id":"1710.10122","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-10-27T13:24:35Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"c5497ca5c716594238321892673baf823f6feb8c110953ea66d57c65af11aae2","abstract_canon_sha256":"32d70f36061360f7ddd759e47ed27fb327740fbaa1b6534fdf248fb200261a20"},"schema_version":"1.0"},"canonical_sha256":"ad533a02708572ff1d4d0eddb7f73fc4064856ac3092aec82b48c09f6040b927","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:54.713587Z","signature_b64":"T/Z1Oue7IpkzEfpj5Xnoj90sXRaX1xEIlYgt1bXHputnMRg6Q5yzfqpaZ7EzezfMRb2vOT/SnOc/GfV8HgtuDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ad533a02708572ff1d4d0eddb7f73fc4064856ac3092aec82b48c09f6040b927","last_reissued_at":"2026-05-18T00:31:54.713042Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:54.713042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.10122","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:31:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ks9d1cQJzZpI+0VrKnrKZI1gbO5C+jyzx56BJar6/Uk+Zu1fcUdJSBfUXsXSlfFSL2dFu+R2Qwi+DmYKv9AEAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T17:04:34.478311Z"},"content_sha256":"5adccddeb40c307ce577c75f97b76d6a390c0446f6bf5d36a93467e70b3882b4","schema_version":"1.0","event_id":"sha256:5adccddeb40c307ce577c75f97b76d6a390c0446f6bf5d36a93467e70b3882b4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:VVJTUATQQVZP6HKNB3O3P5Z7YQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"RRT-CoLearn: towards kinodynamic planning without numerical trajectory optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.RO","authors_text":"Martijn Wisse, Mukunda Bharatheesha, Thomas Moerland, Wouter Wolfslag","submitted_at":"2017-10-27T13:24:35Z","abstract_excerpt":"Sampling-based kinodynamic planners, such as Rapidly-exploring Random Trees (RRTs), pose two fundamental challenges: computing a reliable (pseudo-)metric for the distance between two randomly sampled nodes, and computing a steering input to connect the nodes. The core of these challenges is a Two Point Boundary Value Problem, which is known to be NP-hard. Recently, the distance metric has been approximated using supervised learning, reducing computation time drastically. The previous work on such learning RRTs use direct optimal control to generate the data for supervised learning. This paper "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.10122","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:31:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H0GduNsuV0Gnw6cO2mfpsOKBXVPR9nSSZujx3ytAL2mPIvXn/wIQ6ZxW92n0ramkCNnY3Ytodz7Oeley7hapDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T17:04:34.478690Z"},"content_sha256":"3ce7b0d34e00cd3757bc17730cd31eab3cacb2a7d002fb8b2fd3a0b2c3f8e42a","schema_version":"1.0","event_id":"sha256:3ce7b0d34e00cd3757bc17730cd31eab3cacb2a7d002fb8b2fd3a0b2c3f8e42a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VVJTUATQQVZP6HKNB3O3P5Z7YQ/bundle.json","state_url":"https://pith.science/pith/VVJTUATQQVZP6HKNB3O3P5Z7YQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VVJTUATQQVZP6HKNB3O3P5Z7YQ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-04T17:04:34Z","links":{"resolver":"https://pith.science/pith/VVJTUATQQVZP6HKNB3O3P5Z7YQ","bundle":"https://pith.science/pith/VVJTUATQQVZP6HKNB3O3P5Z7YQ/bundle.json","state":"https://pith.science/pith/VVJTUATQQVZP6HKNB3O3P5Z7YQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VVJTUATQQVZP6HKNB3O3P5Z7YQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:VVJTUATQQVZP6HKNB3O3P5Z7YQ","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":"32d70f36061360f7ddd759e47ed27fb327740fbaa1b6534fdf248fb200261a20","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-10-27T13:24:35Z","title_canon_sha256":"c5497ca5c716594238321892673baf823f6feb8c110953ea66d57c65af11aae2"},"schema_version":"1.0","source":{"id":"1710.10122","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.10122","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"arxiv_version","alias_value":"1710.10122v1","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.10122","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"pith_short_12","alias_value":"VVJTUATQQVZP","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VVJTUATQQVZP6HKN","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VVJTUATQ","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:3ce7b0d34e00cd3757bc17730cd31eab3cacb2a7d002fb8b2fd3a0b2c3f8e42a","target":"graph","created_at":"2026-05-18T00:31:54Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Sampling-based kinodynamic planners, such as Rapidly-exploring Random Trees (RRTs), pose two fundamental challenges: computing a reliable (pseudo-)metric for the distance between two randomly sampled nodes, and computing a steering input to connect the nodes. The core of these challenges is a Two Point Boundary Value Problem, which is known to be NP-hard. Recently, the distance metric has been approximated using supervised learning, reducing computation time drastically. The previous work on such learning RRTs use direct optimal control to generate the data for supervised learning. This paper ","authors_text":"Martijn Wisse, Mukunda Bharatheesha, Thomas Moerland, Wouter Wolfslag","cross_cats":["math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-10-27T13:24:35Z","title":"RRT-CoLearn: towards kinodynamic planning without numerical trajectory optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.10122","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5adccddeb40c307ce577c75f97b76d6a390c0446f6bf5d36a93467e70b3882b4","target":"record","created_at":"2026-05-18T00:31:54Z","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":"32d70f36061360f7ddd759e47ed27fb327740fbaa1b6534fdf248fb200261a20","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-10-27T13:24:35Z","title_canon_sha256":"c5497ca5c716594238321892673baf823f6feb8c110953ea66d57c65af11aae2"},"schema_version":"1.0","source":{"id":"1710.10122","kind":"arxiv","version":1}},"canonical_sha256":"ad533a02708572ff1d4d0eddb7f73fc4064856ac3092aec82b48c09f6040b927","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ad533a02708572ff1d4d0eddb7f73fc4064856ac3092aec82b48c09f6040b927","first_computed_at":"2026-05-18T00:31:54.713042Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:31:54.713042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"T/Z1Oue7IpkzEfpj5Xnoj90sXRaX1xEIlYgt1bXHputnMRg6Q5yzfqpaZ7EzezfMRb2vOT/SnOc/GfV8HgtuDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:31:54.713587Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.10122","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5adccddeb40c307ce577c75f97b76d6a390c0446f6bf5d36a93467e70b3882b4","sha256:3ce7b0d34e00cd3757bc17730cd31eab3cacb2a7d002fb8b2fd3a0b2c3f8e42a"],"state_sha256":"0bbbfa1a72aa8e5cd8895f4c310b3b83e6dc5b5463d2ecb0db39e8cec41e67a2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NSrpBb5gPQSkDxAG7RvcD+09UDVrfX5J9/Ix5MCeiAZ1lYXoRCuOnxWscnF2Ci89NptBaWOYq4b2cUWoCAWvAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T17:04:34.480648Z","bundle_sha256":"abefeb73752da63a28794b6cdcf80dec0ee4e8cf6213738c61032da9a25807bb"}}