{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:U42FI6QYD3OKPR3EVK27A2KKVR","short_pith_number":"pith:U42FI6QY","canonical_record":{"source":{"id":"1903.00228","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-03-01T09:57:31Z","cross_cats_sorted":[],"title_canon_sha256":"95b36c8a5794e6ff45c0329c2e6ce23444a5265cf377357ca72612113a597a63","abstract_canon_sha256":"1751bc44095f8e76bd151982d5eed0b301549364b1afdfeb363b53d337c43b01"},"schema_version":"1.0"},"canonical_sha256":"a734547a181edca7c764aab5f0694aac6df317d5cf976b2ae60c0f9ce35d5b4b","source":{"kind":"arxiv","id":"1903.00228","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.00228","created_at":"2026-05-17T23:52:20Z"},{"alias_kind":"arxiv_version","alias_value":"1903.00228v1","created_at":"2026-05-17T23:52:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.00228","created_at":"2026-05-17T23:52:20Z"},{"alias_kind":"pith_short_12","alias_value":"U42FI6QYD3OK","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"U42FI6QYD3OKPR3E","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"U42FI6QY","created_at":"2026-05-18T12:33:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:U42FI6QYD3OKPR3EVK27A2KKVR","target":"record","payload":{"canonical_record":{"source":{"id":"1903.00228","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-03-01T09:57:31Z","cross_cats_sorted":[],"title_canon_sha256":"95b36c8a5794e6ff45c0329c2e6ce23444a5265cf377357ca72612113a597a63","abstract_canon_sha256":"1751bc44095f8e76bd151982d5eed0b301549364b1afdfeb363b53d337c43b01"},"schema_version":"1.0"},"canonical_sha256":"a734547a181edca7c764aab5f0694aac6df317d5cf976b2ae60c0f9ce35d5b4b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:20.425544Z","signature_b64":"CQiDfzvgFtFNNYEOGmR/wKNrsTZjHRfDvECyp0t7IlCyhljh3rZKu89j7PqfUUuD4uLOPmtS92x8RG0sc3CtDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a734547a181edca7c764aab5f0694aac6df317d5cf976b2ae60c0f9ce35d5b4b","last_reissued_at":"2026-05-17T23:52:20.424772Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:20.424772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.00228","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":"2026-05-17T23:52:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ELgWio1uChTXPe6XgfGwmexdU8CpPpZSUQB4/u5GBgeR4OBGMjmMs0sM5gxPIEm8w/ohlGozwlnsxYfE6Gt7Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T00:58:35.794032Z"},"content_sha256":"b06635d7ddcd551bd05cd8c6089dc769c15103a509a8cbdf649cefefb47a6921","schema_version":"1.0","event_id":"sha256:b06635d7ddcd551bd05cd8c6089dc769c15103a509a8cbdf649cefefb47a6921"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:U42FI6QYD3OKPR3EVK27A2KKVR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving Data Efficiency of Self-supervised Learning for Robotic Grasping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Lars Berscheid, Thomas R\\\"uhr, Torsten Kr\\\"oger","submitted_at":"2019-03-01T09:57:31Z","abstract_excerpt":"Given the task of learning robotic grasping solely based on a depth camera input and gripper force feedback, we derive a learning algorithm from an applied point of view to significantly reduce the amount of required training data. Major improvements in time and data efficiency are achieved by: Firstly, we exploit the geometric consistency between the undistorted depth images and the task space. Using a relative small, fully-convolutional neural network, we predict grasp and gripper parameters with great advantages in training as well as inference performance. Secondly, motivated by the small "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.00228","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":"2026-05-17T23:52:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hCx9IBJMB+LNdpsoqr60FLIDbr5mFC522p26gIkDbXndVe8d44yszekct+2K82SqsBLBzoa7Oyz8fGfi/8xKDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T00:58:35.794400Z"},"content_sha256":"d8da56d995900c00bbcf5438603dec25a352d67b65bfbe7a47041f366c52ec3e","schema_version":"1.0","event_id":"sha256:d8da56d995900c00bbcf5438603dec25a352d67b65bfbe7a47041f366c52ec3e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U42FI6QYD3OKPR3EVK27A2KKVR/bundle.json","state_url":"https://pith.science/pith/U42FI6QYD3OKPR3EVK27A2KKVR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U42FI6QYD3OKPR3EVK27A2KKVR/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":"2026-05-31T00:58:35Z","links":{"resolver":"https://pith.science/pith/U42FI6QYD3OKPR3EVK27A2KKVR","bundle":"https://pith.science/pith/U42FI6QYD3OKPR3EVK27A2KKVR/bundle.json","state":"https://pith.science/pith/U42FI6QYD3OKPR3EVK27A2KKVR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U42FI6QYD3OKPR3EVK27A2KKVR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:U42FI6QYD3OKPR3EVK27A2KKVR","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":"1751bc44095f8e76bd151982d5eed0b301549364b1afdfeb363b53d337c43b01","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-03-01T09:57:31Z","title_canon_sha256":"95b36c8a5794e6ff45c0329c2e6ce23444a5265cf377357ca72612113a597a63"},"schema_version":"1.0","source":{"id":"1903.00228","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.00228","created_at":"2026-05-17T23:52:20Z"},{"alias_kind":"arxiv_version","alias_value":"1903.00228v1","created_at":"2026-05-17T23:52:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.00228","created_at":"2026-05-17T23:52:20Z"},{"alias_kind":"pith_short_12","alias_value":"U42FI6QYD3OK","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"U42FI6QYD3OKPR3E","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"U42FI6QY","created_at":"2026-05-18T12:33:30Z"}],"graph_snapshots":[{"event_id":"sha256:d8da56d995900c00bbcf5438603dec25a352d67b65bfbe7a47041f366c52ec3e","target":"graph","created_at":"2026-05-17T23:52:20Z","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":"Given the task of learning robotic grasping solely based on a depth camera input and gripper force feedback, we derive a learning algorithm from an applied point of view to significantly reduce the amount of required training data. Major improvements in time and data efficiency are achieved by: Firstly, we exploit the geometric consistency between the undistorted depth images and the task space. Using a relative small, fully-convolutional neural network, we predict grasp and gripper parameters with great advantages in training as well as inference performance. Secondly, motivated by the small ","authors_text":"Lars Berscheid, Thomas R\\\"uhr, Torsten Kr\\\"oger","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-03-01T09:57:31Z","title":"Improving Data Efficiency of Self-supervised Learning for Robotic Grasping"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.00228","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:b06635d7ddcd551bd05cd8c6089dc769c15103a509a8cbdf649cefefb47a6921","target":"record","created_at":"2026-05-17T23:52:20Z","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":"1751bc44095f8e76bd151982d5eed0b301549364b1afdfeb363b53d337c43b01","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-03-01T09:57:31Z","title_canon_sha256":"95b36c8a5794e6ff45c0329c2e6ce23444a5265cf377357ca72612113a597a63"},"schema_version":"1.0","source":{"id":"1903.00228","kind":"arxiv","version":1}},"canonical_sha256":"a734547a181edca7c764aab5f0694aac6df317d5cf976b2ae60c0f9ce35d5b4b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a734547a181edca7c764aab5f0694aac6df317d5cf976b2ae60c0f9ce35d5b4b","first_computed_at":"2026-05-17T23:52:20.424772Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:20.424772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CQiDfzvgFtFNNYEOGmR/wKNrsTZjHRfDvECyp0t7IlCyhljh3rZKu89j7PqfUUuD4uLOPmtS92x8RG0sc3CtDg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:20.425544Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.00228","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b06635d7ddcd551bd05cd8c6089dc769c15103a509a8cbdf649cefefb47a6921","sha256:d8da56d995900c00bbcf5438603dec25a352d67b65bfbe7a47041f366c52ec3e"],"state_sha256":"e514c622c8fd32105f0bd1942711353c15f5ecf3d95a02b072f9c592f557bcb3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y6785l7LvWKtPhckaTJaMXKMdDilg6o9ksOb3E6OHjZQT2zYrY1PIoCXAPVcmOEHqcgqLTpAM2Owq/pWQhDJBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T00:58:35.796328Z","bundle_sha256":"9d9d5f1a70d4b4468600240ba4281f28ff42c8e9fe134a9357116adedf7c32f2"}}