{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:YVRPISUEHY4Q3EDWA43WVH3DX6","short_pith_number":"pith:YVRPISUE","canonical_record":{"source":{"id":"1908.03440","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2019-08-08T07:53:24Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"0759df6b19bfe9539740f3499c6d5b5600a6250da596e1257e5d9c1b7b4c0b13","abstract_canon_sha256":"8a8f2d6418d2925b68d15572156026be04c204e1a3d478f76b3cf0127415132d"},"schema_version":"1.0"},"canonical_sha256":"c562f44a843e390d907607376a9f63bfba62b6fa7d0331fadb158045b2d8bdbc","source":{"kind":"arxiv","id":"1908.03440","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.03440","created_at":"2026-07-04T23:52:39Z"},{"alias_kind":"arxiv_version","alias_value":"1908.03440v1","created_at":"2026-07-04T23:52:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.03440","created_at":"2026-07-04T23:52:39Z"},{"alias_kind":"pith_short_12","alias_value":"YVRPISUEHY4Q","created_at":"2026-07-04T23:52:39Z"},{"alias_kind":"pith_short_16","alias_value":"YVRPISUEHY4Q3EDW","created_at":"2026-07-04T23:52:39Z"},{"alias_kind":"pith_short_8","alias_value":"YVRPISUE","created_at":"2026-07-04T23:52:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:YVRPISUEHY4Q3EDWA43WVH3DX6","target":"record","payload":{"canonical_record":{"source":{"id":"1908.03440","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2019-08-08T07:53:24Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"0759df6b19bfe9539740f3499c6d5b5600a6250da596e1257e5d9c1b7b4c0b13","abstract_canon_sha256":"8a8f2d6418d2925b68d15572156026be04c204e1a3d478f76b3cf0127415132d"},"schema_version":"1.0"},"canonical_sha256":"c562f44a843e390d907607376a9f63bfba62b6fa7d0331fadb158045b2d8bdbc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:52:39.945767Z","signature_b64":"IDPwP5mIsp56No2A7XB07hoiVVM54SlAZzknpGjNxslT3oT91aYWdq++zCGXZlkEkAoH5r9f2AAkkSCphYxZBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c562f44a843e390d907607376a9f63bfba62b6fa7d0331fadb158045b2d8bdbc","last_reissued_at":"2026-07-04T23:52:39.945435Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:52:39.945435Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.03440","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":"2026-07-04T23:52:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SrU36PFprOYuPDfHUGIzBIINqpTsqrnq9eDylA4Lii/ExN/iRz+nmycH40L9kbov9+Vow2NiALPzz49+tqoBCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T15:04:37.312043Z"},"content_sha256":"6ce636fc1ff51fa6badfa2793229c0595f9cf596ae6089f4553bd096cd84123f","schema_version":"1.0","event_id":"sha256:6ce636fc1ff51fa6badfa2793229c0595f9cf596ae6089f4553bd096cd84123f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:YVRPISUEHY4Q3EDWA43WVH3DX6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.RO","authors_text":"Alessia Bertugli, Paolo Galeone","submitted_at":"2019-08-08T07:53:24Z","abstract_excerpt":"In this paper, we propose a deep reinforcement learning (DRL) solution to the grasping problem using 2.5D images as the only source of information. In particular, we developed a simulated environment where a robot equipped with a vacuum gripper has the aim of reaching blocks with planar surfaces. These blocks can have different dimensions, shapes, position and orientation. Unity 3D allowed us to simulate a real-world setup, where a depth camera is placed in a fixed position and the stream of images is used by our policy network to learn how to solve the task. We explored different DRL algorith"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.03440","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/1908.03440/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-04T23:52:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J3/FdlSkxbYNjjl5QiMz6xmtoZ8qviVzh2qrxHUMP9QB+AC04CHoGBhtpFEjvSoa9FVUX3wATFYXVo7wcYBQAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T15:04:37.312993Z"},"content_sha256":"d0b0b94a50ac1daae7fc911b55cd56ce3483b9ba1ea750798b882ec3866e53ed","schema_version":"1.0","event_id":"sha256:d0b0b94a50ac1daae7fc911b55cd56ce3483b9ba1ea750798b882ec3866e53ed"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YVRPISUEHY4Q3EDWA43WVH3DX6/bundle.json","state_url":"https://pith.science/pith/YVRPISUEHY4Q3EDWA43WVH3DX6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YVRPISUEHY4Q3EDWA43WVH3DX6/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":"2026-08-16T15:04:37Z","links":{"resolver":"https://pith.science/pith/YVRPISUEHY4Q3EDWA43WVH3DX6","bundle":"https://pith.science/pith/YVRPISUEHY4Q3EDWA43WVH3DX6/bundle.json","state":"https://pith.science/pith/YVRPISUEHY4Q3EDWA43WVH3DX6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YVRPISUEHY4Q3EDWA43WVH3DX6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:YVRPISUEHY4Q3EDWA43WVH3DX6","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":"8a8f2d6418d2925b68d15572156026be04c204e1a3d478f76b3cf0127415132d","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2019-08-08T07:53:24Z","title_canon_sha256":"0759df6b19bfe9539740f3499c6d5b5600a6250da596e1257e5d9c1b7b4c0b13"},"schema_version":"1.0","source":{"id":"1908.03440","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.03440","created_at":"2026-07-04T23:52:39Z"},{"alias_kind":"arxiv_version","alias_value":"1908.03440v1","created_at":"2026-07-04T23:52:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.03440","created_at":"2026-07-04T23:52:39Z"},{"alias_kind":"pith_short_12","alias_value":"YVRPISUEHY4Q","created_at":"2026-07-04T23:52:39Z"},{"alias_kind":"pith_short_16","alias_value":"YVRPISUEHY4Q3EDW","created_at":"2026-07-04T23:52:39Z"},{"alias_kind":"pith_short_8","alias_value":"YVRPISUE","created_at":"2026-07-04T23:52:39Z"}],"graph_snapshots":[{"event_id":"sha256:d0b0b94a50ac1daae7fc911b55cd56ce3483b9ba1ea750798b882ec3866e53ed","target":"graph","created_at":"2026-07-04T23:52:39Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1908.03440/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we propose a deep reinforcement learning (DRL) solution to the grasping problem using 2.5D images as the only source of information. In particular, we developed a simulated environment where a robot equipped with a vacuum gripper has the aim of reaching blocks with planar surfaces. These blocks can have different dimensions, shapes, position and orientation. Unity 3D allowed us to simulate a real-world setup, where a depth camera is placed in a fixed position and the stream of images is used by our policy network to learn how to solve the task. We explored different DRL algorith","authors_text":"Alessia Bertugli, Paolo Galeone","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2019-08-08T07:53:24Z","title":"Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.03440","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":"sha256:6ce636fc1ff51fa6badfa2793229c0595f9cf596ae6089f4553bd096cd84123f","target":"record","created_at":"2026-07-04T23:52:39Z","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":"8a8f2d6418d2925b68d15572156026be04c204e1a3d478f76b3cf0127415132d","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2019-08-08T07:53:24Z","title_canon_sha256":"0759df6b19bfe9539740f3499c6d5b5600a6250da596e1257e5d9c1b7b4c0b13"},"schema_version":"1.0","source":{"id":"1908.03440","kind":"arxiv","version":1}},"canonical_sha256":"c562f44a843e390d907607376a9f63bfba62b6fa7d0331fadb158045b2d8bdbc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c562f44a843e390d907607376a9f63bfba62b6fa7d0331fadb158045b2d8bdbc","first_computed_at":"2026-07-04T23:52:39.945435Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:52:39.945435Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IDPwP5mIsp56No2A7XB07hoiVVM54SlAZzknpGjNxslT3oT91aYWdq++zCGXZlkEkAoH5r9f2AAkkSCphYxZBw==","signature_status":"signed_v1","signed_at":"2026-07-04T23:52:39.945767Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.03440","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6ce636fc1ff51fa6badfa2793229c0595f9cf596ae6089f4553bd096cd84123f","sha256:d0b0b94a50ac1daae7fc911b55cd56ce3483b9ba1ea750798b882ec3866e53ed"],"state_sha256":"18260e58afb21d1a85d4b3f1038e89fa145005d8d36ef13a066a0f06caeb94ea"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gLxuht2APqa0A1vkyqO/XzqzQtCKZeOKWeAF3pGrYJoQwI+ZEm8eWG/CIGT91hZjpvNSyBRLizALltOJ9HYyBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T15:04:37.319762Z","bundle_sha256":"1dde93a6a4dfcb8b18d5326ed5ac52f28e105b77a87562f2ac7017ec9cce6df9"}}