{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:O3ZADQBSDLA2FB37W7RL3PX6IX","short_pith_number":"pith:O3ZADQBS","schema_version":"1.0","canonical_sha256":"76f201c0321ac1a2877fb7e2bdbefe45f04763d8ed3fd90545079d879d700bfe","source":{"kind":"arxiv","id":"2501.17968","version":1},"attestation_state":"computed","paper":{"title":"Online Trajectory Replanner for Dynamically Grasping Irregular Objects","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Anh Nguyen, Florian Grander, Minh Nhat Vu","submitted_at":"2025-01-29T20:06:15Z","abstract_excerpt":"This paper presents a new trajectory replanner for grasping irregular objects. Unlike conventional grasping tasks where the object's geometry is assumed simple, we aim to achieve a \"dynamic grasp\" of the irregular objects, which requires continuous adjustment during the grasping process. To effectively handle irregular objects, we propose a trajectory optimization framework that comprises two phases. Firstly, in a specified time limit of 10s, initial offline trajectories are computed for a seamless motion from an initial configuration of the robot to grasp the object and deliver it to a pre-de"},"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":"2501.17968","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-01-29T20:06:15Z","cross_cats_sorted":[],"title_canon_sha256":"dfb1f6fc86c760aa8ed80e8e57fca1242acc525141cc399ba05d4a4215b700bf","abstract_canon_sha256":"b09d135f0bbf7aa1d14030496e4495b03da6981d9bb18078f7fba753c14d3102"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:07:05.015958Z","signature_b64":"rBi3n7fXXSzCM7zdybt1VnnVAXY6H1ZqPYVcA8cGvhHK2VjM2Nj9nep73/B2ApFC9UdGfFCovjpGtHK92VfpAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"76f201c0321ac1a2877fb7e2bdbefe45f04763d8ed3fd90545079d879d700bfe","last_reissued_at":"2026-07-05T10:07:05.015472Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:07:05.015472Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Online Trajectory Replanner for Dynamically Grasping Irregular Objects","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Anh Nguyen, Florian Grander, Minh Nhat Vu","submitted_at":"2025-01-29T20:06:15Z","abstract_excerpt":"This paper presents a new trajectory replanner for grasping irregular objects. Unlike conventional grasping tasks where the object's geometry is assumed simple, we aim to achieve a \"dynamic grasp\" of the irregular objects, which requires continuous adjustment during the grasping process. To effectively handle irregular objects, we propose a trajectory optimization framework that comprises two phases. Firstly, in a specified time limit of 10s, initial offline trajectories are computed for a seamless motion from an initial configuration of the robot to grasp the object and deliver it to a pre-de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.17968","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/2501.17968/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":"2501.17968","created_at":"2026-07-05T10:07:05.015540+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.17968v1","created_at":"2026-07-05T10:07:05.015540+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.17968","created_at":"2026-07-05T10:07:05.015540+00:00"},{"alias_kind":"pith_short_12","alias_value":"O3ZADQBSDLA2","created_at":"2026-07-05T10:07:05.015540+00:00"},{"alias_kind":"pith_short_16","alias_value":"O3ZADQBSDLA2FB37","created_at":"2026-07-05T10:07:05.015540+00:00"},{"alias_kind":"pith_short_8","alias_value":"O3ZADQBS","created_at":"2026-07-05T10:07:05.015540+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.01304","citing_title":"Towards Autonomous Wood-Log Grasping with a Forestry Crane: Simulator and Benchmarking","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/O3ZADQBSDLA2FB37W7RL3PX6IX","json":"https://pith.science/pith/O3ZADQBSDLA2FB37W7RL3PX6IX.json","graph_json":"https://pith.science/api/pith-number/O3ZADQBSDLA2FB37W7RL3PX6IX/graph.json","events_json":"https://pith.science/api/pith-number/O3ZADQBSDLA2FB37W7RL3PX6IX/events.json","paper":"https://pith.science/paper/O3ZADQBS"},"agent_actions":{"view_html":"https://pith.science/pith/O3ZADQBSDLA2FB37W7RL3PX6IX","download_json":"https://pith.science/pith/O3ZADQBSDLA2FB37W7RL3PX6IX.json","view_paper":"https://pith.science/paper/O3ZADQBS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.17968&json=true","fetch_graph":"https://pith.science/api/pith-number/O3ZADQBSDLA2FB37W7RL3PX6IX/graph.json","fetch_events":"https://pith.science/api/pith-number/O3ZADQBSDLA2FB37W7RL3PX6IX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O3ZADQBSDLA2FB37W7RL3PX6IX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O3ZADQBSDLA2FB37W7RL3PX6IX/action/storage_attestation","attest_author":"https://pith.science/pith/O3ZADQBSDLA2FB37W7RL3PX6IX/action/author_attestation","sign_citation":"https://pith.science/pith/O3ZADQBSDLA2FB37W7RL3PX6IX/action/citation_signature","submit_replication":"https://pith.science/pith/O3ZADQBSDLA2FB37W7RL3PX6IX/action/replication_record"}},"created_at":"2026-07-05T10:07:05.015540+00:00","updated_at":"2026-07-05T10:07:05.015540+00:00"}