{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:5GW3SCLVJRR4IWNZJAIEULAOAB","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":"b5770fa3a46a296b45aea5ac4a6e109d7a560186459e7aac391b08c24ad4c86e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-06-25T07:32:25Z","title_canon_sha256":"2a987a99994c2385079e03ce0d3ebe61b2183af2a98aaf4f9337160f439cb529"},"schema_version":"1.0","source":{"id":"2606.26700","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.26700","created_at":"2026-06-26T01:15:57Z"},{"alias_kind":"arxiv_version","alias_value":"2606.26700v1","created_at":"2026-06-26T01:15:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.26700","created_at":"2026-06-26T01:15:57Z"},{"alias_kind":"pith_short_12","alias_value":"5GW3SCLVJRR4","created_at":"2026-06-26T01:15:57Z"},{"alias_kind":"pith_short_16","alias_value":"5GW3SCLVJRR4IWNZ","created_at":"2026-06-26T01:15:57Z"},{"alias_kind":"pith_short_8","alias_value":"5GW3SCLV","created_at":"2026-06-26T01:15:57Z"}],"graph_snapshots":[{"event_id":"sha256:97658e46f4a028f57ef39a4320c5a601fbab4f9de4d978410a433f559d46dcdf","target":"graph","created_at":"2026-06-26T01:15:57Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.26700/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Motion feasibility prediction plays a central role in robotics, particularly in task and motion planning and manipulation. A major bottleneck for this problem in cluttered environments is that infeasible planning attempts by Sampling-based motion planners (SBMPs) can incur substantial computational cost. Also existing approaches for infeasibility certification are limited to low-dimensional configuration spaces and often assume simplified geometric environments represented by primitive objects with known parameters. We study the complementary problem of learning motion feasibility prediction d","authors_text":"Antony Thomas, Arthi, Girish Varma, Sajid Ansari","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-06-25T07:32:25Z","title":"Learning Motion Feasibility from Point Clouds in Cluttered Environments"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.26700","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":"sha256:49cc232ef893852e72ab797aa327034c53021ef3be3bf322a5fc57900dc65596","target":"record","created_at":"2026-06-26T01:15:57Z","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":"b5770fa3a46a296b45aea5ac4a6e109d7a560186459e7aac391b08c24ad4c86e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-06-25T07:32:25Z","title_canon_sha256":"2a987a99994c2385079e03ce0d3ebe61b2183af2a98aaf4f9337160f439cb529"},"schema_version":"1.0","source":{"id":"2606.26700","kind":"arxiv","version":1}},"canonical_sha256":"e9adb909754c63c459b948104a2c0e007b49056a51940ce99b33e9877fc74017","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e9adb909754c63c459b948104a2c0e007b49056a51940ce99b33e9877fc74017","first_computed_at":"2026-06-26T01:15:57.221584Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-26T01:15:57.221584Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"w9u/RKMIc84oI3zXyM25ljYV8GECRdrViDyERJzS7csS+YnEB4MUaEnFF64CfERFvs5Cl0/NTbH1pzwjnXBpDQ==","signature_status":"signed_v1","signed_at":"2026-06-26T01:15:57.222007Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.26700","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:49cc232ef893852e72ab797aa327034c53021ef3be3bf322a5fc57900dc65596","sha256:97658e46f4a028f57ef39a4320c5a601fbab4f9de4d978410a433f559d46dcdf"],"state_sha256":"fadb0578c92be303defc709f9442b5d04ebfd8ec08f4f9eb255a6029423db9bd"}