{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:SSGJAOFBJQC5HMIDLPYQ33MIKZ","short_pith_number":"pith:SSGJAOFB","canonical_record":{"source":{"id":"1806.09266","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-06-25T03:08:28Z","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"title_canon_sha256":"028d14ccd2480747f2165372bfb6f3f7fd26a0001110fb8b5a783f0001aa78a6","abstract_canon_sha256":"614eb3a519d66f4301f77a01207f51056d3700b567825c84b9a31564919bc299"},"schema_version":"1.0"},"canonical_sha256":"948c9038a14c05d3b1035bf10ded8856525c26698934767acd9706b858a09fc9","source":{"kind":"arxiv","id":"1806.09266","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.09266","created_at":"2026-05-18T00:12:29Z"},{"alias_kind":"arxiv_version","alias_value":"1806.09266v1","created_at":"2026-05-18T00:12:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.09266","created_at":"2026-05-18T00:12:29Z"},{"alias_kind":"pith_short_12","alias_value":"SSGJAOFBJQC5","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"SSGJAOFBJQC5HMID","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"SSGJAOFB","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:SSGJAOFBJQC5HMIDLPYQ33MIKZ","target":"record","payload":{"canonical_record":{"source":{"id":"1806.09266","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-06-25T03:08:28Z","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"title_canon_sha256":"028d14ccd2480747f2165372bfb6f3f7fd26a0001110fb8b5a783f0001aa78a6","abstract_canon_sha256":"614eb3a519d66f4301f77a01207f51056d3700b567825c84b9a31564919bc299"},"schema_version":"1.0"},"canonical_sha256":"948c9038a14c05d3b1035bf10ded8856525c26698934767acd9706b858a09fc9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:29.821665Z","signature_b64":"UOap2yExmn/XAFkPsPVL108AWsH8INS3yZxT+5np94je6vUcojhxmNm00F+k0YrgnT3Ujq+kisUgSlCoXPlABw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"948c9038a14c05d3b1035bf10ded8856525c26698934767acd9706b858a09fc9","last_reissued_at":"2026-05-18T00:12:29.821039Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:29.821039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.09266","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":"2026-05-18T00:12:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XD7SRczbHphULfEhJIi465IanxvZ2+pmQdvyIQxkd4TxM1E8n8+sGiD1VH69YNQkaY7FKspoySsGqhQWtWmWDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-18T23:51:41.753351Z"},"content_sha256":"c5be924fdc82945b2bc0f2f291d1beccecc38432f43d2faa445421e1594ece3e","schema_version":"1.0","event_id":"sha256:c5be924fdc82945b2bc0f2f291d1beccecc38432f43d2faa445421e1594ece3e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:SSGJAOFBJQC5HMIDLPYQ33MIKZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","stat.ML"],"primary_cat":"cs.RO","authors_text":"Andrey Kurenkov, Animesh Garg, Kuan Fang, Li Fei-Fei, Silvio Savarese, Viraj Mehta, Yuke Zhu","submitted_at":"2018-06-25T03:08:28Z","abstract_excerpt":"Tool manipulation is vital for facilitating robots to complete challenging task goals. It requires reasoning about the desired effect of the task and thus properly grasping and manipulating the tool to achieve the task. Task-agnostic grasping optimizes for grasp robustness while ignoring crucial task-specific constraints. In this paper, we propose the Task-Oriented Grasping Network (TOG-Net) to jointly optimize both task-oriented grasping of a tool and the manipulation policy for that tool. The training process of the model is based on large-scale simulated self-supervision with procedurally g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.09266","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":"2026-05-18T00:12:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e2AzdIShmn0oFhchMn8jQLcQTWSk82b/DvSmL4roXmB48HA1XtxaLhv+V3ivNn87xaCxUPos3hrNL/6DgWrOBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-18T23:51:41.754352Z"},"content_sha256":"d5242b38bf8d6f9364a16789764cf49a8077205c7ace5e15302f150c3248e1e1","schema_version":"1.0","event_id":"sha256:d5242b38bf8d6f9364a16789764cf49a8077205c7ace5e15302f150c3248e1e1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SSGJAOFBJQC5HMIDLPYQ33MIKZ/bundle.json","state_url":"https://pith.science/pith/SSGJAOFBJQC5HMIDLPYQ33MIKZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SSGJAOFBJQC5HMIDLPYQ33MIKZ/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":"2026-05-18T23:51:41Z","links":{"resolver":"https://pith.science/pith/SSGJAOFBJQC5HMIDLPYQ33MIKZ","bundle":"https://pith.science/pith/SSGJAOFBJQC5HMIDLPYQ33MIKZ/bundle.json","state":"https://pith.science/pith/SSGJAOFBJQC5HMIDLPYQ33MIKZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SSGJAOFBJQC5HMIDLPYQ33MIKZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:SSGJAOFBJQC5HMIDLPYQ33MIKZ","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":"614eb3a519d66f4301f77a01207f51056d3700b567825c84b9a31564919bc299","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-06-25T03:08:28Z","title_canon_sha256":"028d14ccd2480747f2165372bfb6f3f7fd26a0001110fb8b5a783f0001aa78a6"},"schema_version":"1.0","source":{"id":"1806.09266","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.09266","created_at":"2026-05-18T00:12:29Z"},{"alias_kind":"arxiv_version","alias_value":"1806.09266v1","created_at":"2026-05-18T00:12:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.09266","created_at":"2026-05-18T00:12:29Z"},{"alias_kind":"pith_short_12","alias_value":"SSGJAOFBJQC5","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"SSGJAOFBJQC5HMID","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"SSGJAOFB","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:d5242b38bf8d6f9364a16789764cf49a8077205c7ace5e15302f150c3248e1e1","target":"graph","created_at":"2026-05-18T00:12:29Z","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":"Tool manipulation is vital for facilitating robots to complete challenging task goals. It requires reasoning about the desired effect of the task and thus properly grasping and manipulating the tool to achieve the task. Task-agnostic grasping optimizes for grasp robustness while ignoring crucial task-specific constraints. In this paper, we propose the Task-Oriented Grasping Network (TOG-Net) to jointly optimize both task-oriented grasping of a tool and the manipulation policy for that tool. The training process of the model is based on large-scale simulated self-supervision with procedurally g","authors_text":"Andrey Kurenkov, Animesh Garg, Kuan Fang, Li Fei-Fei, Silvio Savarese, Viraj Mehta, Yuke Zhu","cross_cats":["cs.CV","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-06-25T03:08:28Z","title":"Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.09266","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:c5be924fdc82945b2bc0f2f291d1beccecc38432f43d2faa445421e1594ece3e","target":"record","created_at":"2026-05-18T00:12:29Z","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":"614eb3a519d66f4301f77a01207f51056d3700b567825c84b9a31564919bc299","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-06-25T03:08:28Z","title_canon_sha256":"028d14ccd2480747f2165372bfb6f3f7fd26a0001110fb8b5a783f0001aa78a6"},"schema_version":"1.0","source":{"id":"1806.09266","kind":"arxiv","version":1}},"canonical_sha256":"948c9038a14c05d3b1035bf10ded8856525c26698934767acd9706b858a09fc9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"948c9038a14c05d3b1035bf10ded8856525c26698934767acd9706b858a09fc9","first_computed_at":"2026-05-18T00:12:29.821039Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:12:29.821039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UOap2yExmn/XAFkPsPVL108AWsH8INS3yZxT+5np94je6vUcojhxmNm00F+k0YrgnT3Ujq+kisUgSlCoXPlABw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:12:29.821665Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.09266","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c5be924fdc82945b2bc0f2f291d1beccecc38432f43d2faa445421e1594ece3e","sha256:d5242b38bf8d6f9364a16789764cf49a8077205c7ace5e15302f150c3248e1e1"],"state_sha256":"db45cbf0e58036870d382415c9b42c99f92e6c120633bb7e327942955c910a99"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0+v7/VoikhMnseMlpvb5xHlO5/8EKP1iF2MGYKp9oe2T1bOuIOD6YqS2dDLkuALDZE/LqKI60CqvBEstv7nVCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-18T23:51:41.756383Z","bundle_sha256":"0b8aa4945af0270d70a5d22137ee466d91aac8d5cd5f2234e3add054ea5d51cc"}}