{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:MHJUF2WJFMP64QGAWXLNJCS2AS","short_pith_number":"pith:MHJUF2WJ","canonical_record":{"source":{"id":"1901.02992","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-01-10T01:36:55Z","cross_cats_sorted":[],"title_canon_sha256":"b573effc15f832a3781d64e5badb2fba7b71101205e884ebbc2407803bdfc2da","abstract_canon_sha256":"3d41019f9f3ca97732db6b8d6d20ba82e30325962461dd196df7b5b1e9419465"},"schema_version":"1.0"},"canonical_sha256":"61d342eac92b1fee40c0b5d6d48a5a04a3b259a215b06ac11e98aee093f8e6df","source":{"kind":"arxiv","id":"1901.02992","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.02992","created_at":"2026-05-17T23:56:35Z"},{"alias_kind":"arxiv_version","alias_value":"1901.02992v1","created_at":"2026-05-17T23:56:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.02992","created_at":"2026-05-17T23:56:35Z"},{"alias_kind":"pith_short_12","alias_value":"MHJUF2WJFMP6","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"MHJUF2WJFMP64QGA","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"MHJUF2WJ","created_at":"2026-05-18T12:33:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:MHJUF2WJFMP64QGAWXLNJCS2AS","target":"record","payload":{"canonical_record":{"source":{"id":"1901.02992","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-01-10T01:36:55Z","cross_cats_sorted":[],"title_canon_sha256":"b573effc15f832a3781d64e5badb2fba7b71101205e884ebbc2407803bdfc2da","abstract_canon_sha256":"3d41019f9f3ca97732db6b8d6d20ba82e30325962461dd196df7b5b1e9419465"},"schema_version":"1.0"},"canonical_sha256":"61d342eac92b1fee40c0b5d6d48a5a04a3b259a215b06ac11e98aee093f8e6df","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:35.853343Z","signature_b64":"fJL3V7pV8HrIAJaqv4keTRB7BYbVWWNpd47XfY4u9XgHsIO4WdpSXer9U9PZPOWHlAm/AiIxRbIKPR7+B1hYAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"61d342eac92b1fee40c0b5d6d48a5a04a3b259a215b06ac11e98aee093f8e6df","last_reissued_at":"2026-05-17T23:56:35.852719Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:35.852719Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.02992","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":"2026-05-17T23:56:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3BJTNMvxRHVbBKNeJzPSWQwie54ym6+Oy0a8Qf8yDYFrNrvEchvoy/650z/RyMBk47MCpUI8uCxujVyBAP8hDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T17:19:31.287378Z"},"content_sha256":"538fcb44c95a3dd91346158d5c848d9ad5bba410af2fea2a574820b9782da68e","schema_version":"1.0","event_id":"sha256:538fcb44c95a3dd91346158d5c848d9ad5bba410af2fea2a574820b9782da68e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:MHJUF2WJFMP64QGAWXLNJCS2AS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Modeling Grasp Type Improves Learning-Based Grasp Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Qingkai Lu, Tucker Hermans","submitted_at":"2019-01-10T01:36:55Z","abstract_excerpt":"Different manipulation tasks require different types of grasps. For example, holding a heavy tool like a hammer requires a multi-fingered power grasp offering stability, while holding a pen to write requires a multi-fingered precision grasp to impart dexterity on the object. In this paper, we propose a probabilistic grasp planner that explicitly models grasp type for planning high-quality precision and power grasps in real-time. We take a learning approach in order to plan grasps of different types for previously unseen objects when only partial visual information is available. Our work demons"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.02992","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":"2026-05-17T23:56:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KiOaUuTu6+0C3/jvwwImoG5nFcNIUMGn1XSQ0vTU6cFfbFUvNKDs47+txQKE7bT1frOSDh9Zdzai1O6uRxvIDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T17:19:31.288051Z"},"content_sha256":"657702bc43a7306ae8f7ff670c78476c7587180b3e140fbe0a546f86ad7c2412","schema_version":"1.0","event_id":"sha256:657702bc43a7306ae8f7ff670c78476c7587180b3e140fbe0a546f86ad7c2412"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MHJUF2WJFMP64QGAWXLNJCS2AS/bundle.json","state_url":"https://pith.science/pith/MHJUF2WJFMP64QGAWXLNJCS2AS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MHJUF2WJFMP64QGAWXLNJCS2AS/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":"2026-06-07T17:19:31Z","links":{"resolver":"https://pith.science/pith/MHJUF2WJFMP64QGAWXLNJCS2AS","bundle":"https://pith.science/pith/MHJUF2WJFMP64QGAWXLNJCS2AS/bundle.json","state":"https://pith.science/pith/MHJUF2WJFMP64QGAWXLNJCS2AS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MHJUF2WJFMP64QGAWXLNJCS2AS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:MHJUF2WJFMP64QGAWXLNJCS2AS","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":"3d41019f9f3ca97732db6b8d6d20ba82e30325962461dd196df7b5b1e9419465","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-01-10T01:36:55Z","title_canon_sha256":"b573effc15f832a3781d64e5badb2fba7b71101205e884ebbc2407803bdfc2da"},"schema_version":"1.0","source":{"id":"1901.02992","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.02992","created_at":"2026-05-17T23:56:35Z"},{"alias_kind":"arxiv_version","alias_value":"1901.02992v1","created_at":"2026-05-17T23:56:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.02992","created_at":"2026-05-17T23:56:35Z"},{"alias_kind":"pith_short_12","alias_value":"MHJUF2WJFMP6","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"MHJUF2WJFMP64QGA","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"MHJUF2WJ","created_at":"2026-05-18T12:33:21Z"}],"graph_snapshots":[{"event_id":"sha256:657702bc43a7306ae8f7ff670c78476c7587180b3e140fbe0a546f86ad7c2412","target":"graph","created_at":"2026-05-17T23:56:35Z","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":"Different manipulation tasks require different types of grasps. For example, holding a heavy tool like a hammer requires a multi-fingered power grasp offering stability, while holding a pen to write requires a multi-fingered precision grasp to impart dexterity on the object. In this paper, we propose a probabilistic grasp planner that explicitly models grasp type for planning high-quality precision and power grasps in real-time. We take a learning approach in order to plan grasps of different types for previously unseen objects when only partial visual information is available. Our work demons","authors_text":"Qingkai Lu, Tucker Hermans","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-01-10T01:36:55Z","title":"Modeling Grasp Type Improves Learning-Based Grasp Planning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.02992","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":"sha256:538fcb44c95a3dd91346158d5c848d9ad5bba410af2fea2a574820b9782da68e","target":"record","created_at":"2026-05-17T23:56:35Z","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":"3d41019f9f3ca97732db6b8d6d20ba82e30325962461dd196df7b5b1e9419465","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-01-10T01:36:55Z","title_canon_sha256":"b573effc15f832a3781d64e5badb2fba7b71101205e884ebbc2407803bdfc2da"},"schema_version":"1.0","source":{"id":"1901.02992","kind":"arxiv","version":1}},"canonical_sha256":"61d342eac92b1fee40c0b5d6d48a5a04a3b259a215b06ac11e98aee093f8e6df","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"61d342eac92b1fee40c0b5d6d48a5a04a3b259a215b06ac11e98aee093f8e6df","first_computed_at":"2026-05-17T23:56:35.852719Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:56:35.852719Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fJL3V7pV8HrIAJaqv4keTRB7BYbVWWNpd47XfY4u9XgHsIO4WdpSXer9U9PZPOWHlAm/AiIxRbIKPR7+B1hYAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:56:35.853343Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.02992","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:538fcb44c95a3dd91346158d5c848d9ad5bba410af2fea2a574820b9782da68e","sha256:657702bc43a7306ae8f7ff670c78476c7587180b3e140fbe0a546f86ad7c2412"],"state_sha256":"9cd00e6fbf8137fbcb3cafd53cdb48b40c52f0796542cffcfa4d83a708ff8748"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P+l6S8UrtWbaFEjZggzdjVbboKW/t5/kjk1jXpWoL99eL3bWlezowp3sRoAFZc1ZkYwHNVzPuFUTIoipXdKECw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T17:19:31.291517Z","bundle_sha256":"11fa9880a1dd205edd019406d7fe8d87fbe3cdf3ab46ff71a8b32cb659b552f8"}}