{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XXBTMLZ54PP225AER357WZCTJS","short_pith_number":"pith:XXBTMLZ5","schema_version":"1.0","canonical_sha256":"bdc3362f3de3dfad74048efbfb64534ca06b1c7f93efd4d5dcace9725e21dc01","source":{"kind":"arxiv","id":"2407.11436","version":1},"attestation_state":"computed","paper":{"title":"APriCoT: Action Primitives based on Contact-state Transition for In-Hand Tool Manipulation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Atsushi Kanehira, Daichi Saito, Hideki Koike, Jun Takamatsu, Katsushi Ikeuchi, Kazuhiro Sasabuchi, Naoki Wake","submitted_at":"2024-07-16T07:02:39Z","abstract_excerpt":"In-hand tool manipulation is an operation that not only manipulates a tool within the hand (i.e., in-hand manipulation) but also achieves a grasp suitable for a task after the manipulation. This study aims to achieve an in-hand tool manipulation skill through deep reinforcement learning. The difficulty of learning the skill arises because this manipulation requires (A) exploring long-term contact-state changes to achieve the desired grasp and (B) highly-varied motions depending on the contact-state transition. (A) leads to a sparsity of a reward on a successful grasp, and (B) requires an RL ag"},"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":"2407.11436","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-07-16T07:02:39Z","cross_cats_sorted":[],"title_canon_sha256":"472fc92d2bca6e705f6b0ab975e85b1378ada9ae900e5dc1d124ccbe48acb906","abstract_canon_sha256":"98480df1e0dacafa28fbcbf1db4caeb6388db17fe29c235cfd6f8d633fd0a22d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:44:24.528918Z","signature_b64":"HfS+n6HEBYVgU6om0IrbwPX+qInNsr3HAV09X4d1kgh1MFoHvx6jXv0oez4EhFZRbktcK4IzpTuvtowCbNreBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bdc3362f3de3dfad74048efbfb64534ca06b1c7f93efd4d5dcace9725e21dc01","last_reissued_at":"2026-07-05T08:44:24.528542Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:44:24.528542Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"APriCoT: Action Primitives based on Contact-state Transition for In-Hand Tool Manipulation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Atsushi Kanehira, Daichi Saito, Hideki Koike, Jun Takamatsu, Katsushi Ikeuchi, Kazuhiro Sasabuchi, Naoki Wake","submitted_at":"2024-07-16T07:02:39Z","abstract_excerpt":"In-hand tool manipulation is an operation that not only manipulates a tool within the hand (i.e., in-hand manipulation) but also achieves a grasp suitable for a task after the manipulation. This study aims to achieve an in-hand tool manipulation skill through deep reinforcement learning. The difficulty of learning the skill arises because this manipulation requires (A) exploring long-term contact-state changes to achieve the desired grasp and (B) highly-varied motions depending on the contact-state transition. (A) leads to a sparsity of a reward on a successful grasp, and (B) requires an RL ag"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.11436","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/2407.11436/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":"2407.11436","created_at":"2026-07-05T08:44:24.528602+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.11436v1","created_at":"2026-07-05T08:44:24.528602+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.11436","created_at":"2026-07-05T08:44:24.528602+00:00"},{"alias_kind":"pith_short_12","alias_value":"XXBTMLZ54PP2","created_at":"2026-07-05T08:44:24.528602+00:00"},{"alias_kind":"pith_short_16","alias_value":"XXBTMLZ54PP225AE","created_at":"2026-07-05T08:44:24.528602+00:00"},{"alias_kind":"pith_short_8","alias_value":"XXBTMLZ5","created_at":"2026-07-05T08:44:24.528602+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XXBTMLZ54PP225AER357WZCTJS","json":"https://pith.science/pith/XXBTMLZ54PP225AER357WZCTJS.json","graph_json":"https://pith.science/api/pith-number/XXBTMLZ54PP225AER357WZCTJS/graph.json","events_json":"https://pith.science/api/pith-number/XXBTMLZ54PP225AER357WZCTJS/events.json","paper":"https://pith.science/paper/XXBTMLZ5"},"agent_actions":{"view_html":"https://pith.science/pith/XXBTMLZ54PP225AER357WZCTJS","download_json":"https://pith.science/pith/XXBTMLZ54PP225AER357WZCTJS.json","view_paper":"https://pith.science/paper/XXBTMLZ5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.11436&json=true","fetch_graph":"https://pith.science/api/pith-number/XXBTMLZ54PP225AER357WZCTJS/graph.json","fetch_events":"https://pith.science/api/pith-number/XXBTMLZ54PP225AER357WZCTJS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XXBTMLZ54PP225AER357WZCTJS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XXBTMLZ54PP225AER357WZCTJS/action/storage_attestation","attest_author":"https://pith.science/pith/XXBTMLZ54PP225AER357WZCTJS/action/author_attestation","sign_citation":"https://pith.science/pith/XXBTMLZ54PP225AER357WZCTJS/action/citation_signature","submit_replication":"https://pith.science/pith/XXBTMLZ54PP225AER357WZCTJS/action/replication_record"}},"created_at":"2026-07-05T08:44:24.528602+00:00","updated_at":"2026-07-05T08:44:24.528602+00:00"}