{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:DISH5OJ4ST5KAULPDKL6ROJPPZ","short_pith_number":"pith:DISH5OJ4","canonical_record":{"source":{"id":"2411.04386","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-11-07T03:00:12Z","cross_cats_sorted":[],"title_canon_sha256":"15b7e35ebef340268dd6cf11e0365110b5b2fcccc9c767fb3a7fa97ef0127f2e","abstract_canon_sha256":"c41ceae5490be422fa6dd73f4cf5d442d0b5432eae5a7105b23b93b7c1b500aa"},"schema_version":"1.0"},"canonical_sha256":"1a247eb93c94faa0516f1a97e8b92f7e75ebfe2746d5474a159361d6d8b0b7bc","source":{"kind":"arxiv","id":"2411.04386","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.04386","created_at":"2026-07-05T10:46:58Z"},{"alias_kind":"arxiv_version","alias_value":"2411.04386v3","created_at":"2026-07-05T10:46:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.04386","created_at":"2026-07-05T10:46:58Z"},{"alias_kind":"pith_short_12","alias_value":"DISH5OJ4ST5K","created_at":"2026-07-05T10:46:58Z"},{"alias_kind":"pith_short_16","alias_value":"DISH5OJ4ST5KAULP","created_at":"2026-07-05T10:46:58Z"},{"alias_kind":"pith_short_8","alias_value":"DISH5OJ4","created_at":"2026-07-05T10:46:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:DISH5OJ4ST5KAULPDKL6ROJPPZ","target":"record","payload":{"canonical_record":{"source":{"id":"2411.04386","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-11-07T03:00:12Z","cross_cats_sorted":[],"title_canon_sha256":"15b7e35ebef340268dd6cf11e0365110b5b2fcccc9c767fb3a7fa97ef0127f2e","abstract_canon_sha256":"c41ceae5490be422fa6dd73f4cf5d442d0b5432eae5a7105b23b93b7c1b500aa"},"schema_version":"1.0"},"canonical_sha256":"1a247eb93c94faa0516f1a97e8b92f7e75ebfe2746d5474a159361d6d8b0b7bc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:46:58.166036Z","signature_b64":"dyW8q3ShR7JPBIwiR1VHy/M8QbiBL36B9gVkFrpyUOc54asLkmydzC4C86t/ahrT1i49DpqdM4x5tqlAqBeHDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a247eb93c94faa0516f1a97e8b92f7e75ebfe2746d5474a159361d6d8b0b7bc","last_reissued_at":"2026-07-05T10:46:58.165538Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:46:58.165538Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.04386","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:46:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y/XM3CGp+eVfrDk/KTZvlqu0dRVqUIU5+gk+Tuxwa5iUcXxLhzd8pKUQiy7PRGDAQqj5iOPdp+qA2v5BE7sYCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T03:12:29.768033Z"},"content_sha256":"469ee6d29614c45b0439389183dfd384fa5607fc3f1252635e6d1760e562869d","schema_version":"1.0","event_id":"sha256:469ee6d29614c45b0439389183dfd384fa5607fc3f1252635e6d1760e562869d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:DISH5OJ4ST5KAULPDKL6ROJPPZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SuperQ-GRASP: Superquadrics-based Grasp Pose Estimation on Larger Objects for Mobile-Manipulation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Karthik Desingh, Xun Tu","submitted_at":"2024-11-07T03:00:12Z","abstract_excerpt":"Grasp planning and estimation have been a longstanding research problem in robotics, with two main approaches to find graspable poses on the objects: 1) geometric approach, which relies on 3D models of objects and the gripper to estimate valid grasp poses, and 2) data-driven, learning-based approach, with models trained to identify grasp poses from raw sensor observations. The latter assumes comprehensive geometric coverage during the training phase. However, the data-driven approach is typically biased toward tabletop scenarios and struggle to generalize to out-of-distribution scenarios with "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.04386","kind":"arxiv","version":3},"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/2411.04386/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:46:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I2cFM6AHOkt4l5P2zCjAKDVpgwUzcIV1WIarxmW42gPaR3V/or1pcZEFeQ2sgGhkAwQLzBufroBaZYV2Org+AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T03:12:29.768456Z"},"content_sha256":"5564e1903d4aeffba1212927847d944fc9feeeaca78489517f4958de8e19e27f","schema_version":"1.0","event_id":"sha256:5564e1903d4aeffba1212927847d944fc9feeeaca78489517f4958de8e19e27f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DISH5OJ4ST5KAULPDKL6ROJPPZ/bundle.json","state_url":"https://pith.science/pith/DISH5OJ4ST5KAULPDKL6ROJPPZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DISH5OJ4ST5KAULPDKL6ROJPPZ/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":"2026-07-10T03:12:29Z","links":{"resolver":"https://pith.science/pith/DISH5OJ4ST5KAULPDKL6ROJPPZ","bundle":"https://pith.science/pith/DISH5OJ4ST5KAULPDKL6ROJPPZ/bundle.json","state":"https://pith.science/pith/DISH5OJ4ST5KAULPDKL6ROJPPZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DISH5OJ4ST5KAULPDKL6ROJPPZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:DISH5OJ4ST5KAULPDKL6ROJPPZ","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":"c41ceae5490be422fa6dd73f4cf5d442d0b5432eae5a7105b23b93b7c1b500aa","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-11-07T03:00:12Z","title_canon_sha256":"15b7e35ebef340268dd6cf11e0365110b5b2fcccc9c767fb3a7fa97ef0127f2e"},"schema_version":"1.0","source":{"id":"2411.04386","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.04386","created_at":"2026-07-05T10:46:58Z"},{"alias_kind":"arxiv_version","alias_value":"2411.04386v3","created_at":"2026-07-05T10:46:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.04386","created_at":"2026-07-05T10:46:58Z"},{"alias_kind":"pith_short_12","alias_value":"DISH5OJ4ST5K","created_at":"2026-07-05T10:46:58Z"},{"alias_kind":"pith_short_16","alias_value":"DISH5OJ4ST5KAULP","created_at":"2026-07-05T10:46:58Z"},{"alias_kind":"pith_short_8","alias_value":"DISH5OJ4","created_at":"2026-07-05T10:46:58Z"}],"graph_snapshots":[{"event_id":"sha256:5564e1903d4aeffba1212927847d944fc9feeeaca78489517f4958de8e19e27f","target":"graph","created_at":"2026-07-05T10:46:58Z","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/2411.04386/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Grasp planning and estimation have been a longstanding research problem in robotics, with two main approaches to find graspable poses on the objects: 1) geometric approach, which relies on 3D models of objects and the gripper to estimate valid grasp poses, and 2) data-driven, learning-based approach, with models trained to identify grasp poses from raw sensor observations. The latter assumes comprehensive geometric coverage during the training phase. However, the data-driven approach is typically biased toward tabletop scenarios and struggle to generalize to out-of-distribution scenarios with ","authors_text":"Karthik Desingh, Xun Tu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-11-07T03:00:12Z","title":"SuperQ-GRASP: Superquadrics-based Grasp Pose Estimation on Larger Objects for Mobile-Manipulation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.04386","kind":"arxiv","version":3},"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":"sha256:469ee6d29614c45b0439389183dfd384fa5607fc3f1252635e6d1760e562869d","target":"record","created_at":"2026-07-05T10:46:58Z","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":"c41ceae5490be422fa6dd73f4cf5d442d0b5432eae5a7105b23b93b7c1b500aa","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-11-07T03:00:12Z","title_canon_sha256":"15b7e35ebef340268dd6cf11e0365110b5b2fcccc9c767fb3a7fa97ef0127f2e"},"schema_version":"1.0","source":{"id":"2411.04386","kind":"arxiv","version":3}},"canonical_sha256":"1a247eb93c94faa0516f1a97e8b92f7e75ebfe2746d5474a159361d6d8b0b7bc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1a247eb93c94faa0516f1a97e8b92f7e75ebfe2746d5474a159361d6d8b0b7bc","first_computed_at":"2026-07-05T10:46:58.165538Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:46:58.165538Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dyW8q3ShR7JPBIwiR1VHy/M8QbiBL36B9gVkFrpyUOc54asLkmydzC4C86t/ahrT1i49DpqdM4x5tqlAqBeHDg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:46:58.166036Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.04386","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:469ee6d29614c45b0439389183dfd384fa5607fc3f1252635e6d1760e562869d","sha256:5564e1903d4aeffba1212927847d944fc9feeeaca78489517f4958de8e19e27f"],"state_sha256":"70a29c3e376a14a628bbdeb89e4a12d4321b2b55d9dba4b48e2be39af476e82c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0zqSSTt2daWOAMfuquR5eIpQQjg+Rw6i5BQvGxEY0s7CFkZtY4BBx9LE189GaJ9TMPCqzq6MEKj8s3OKmxpCDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-10T03:12:29.770601Z","bundle_sha256":"1d9c2066b722dd29864aec389db640e5e6a71513ae42a522f6b9dcb5b8bf4dff"}}