{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:CL2IVGRYKVEEKBLQRX4G4MM2DY","short_pith_number":"pith:CL2IVGRY","canonical_record":{"source":{"id":"2210.08126","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2022-10-14T21:46:20Z","cross_cats_sorted":[],"title_canon_sha256":"940aeb476640feb9fad9d2b0a62879c24c5721e17135e9e784e8e8280edf80ec","abstract_canon_sha256":"7e9771700407edbe16a94cc4e6d22ec5f87b59baecbe2fafc610f16dcb224eb6"},"schema_version":"1.0"},"canonical_sha256":"12f48a9a3855484505708df86e319a1e30d14f23f280f9a79d4494595a201df1","source":{"kind":"arxiv","id":"2210.08126","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.08126","created_at":"2026-07-05T06:50:27Z"},{"alias_kind":"arxiv_version","alias_value":"2210.08126v2","created_at":"2026-07-05T06:50:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.08126","created_at":"2026-07-05T06:50:27Z"},{"alias_kind":"pith_short_12","alias_value":"CL2IVGRYKVEE","created_at":"2026-07-05T06:50:27Z"},{"alias_kind":"pith_short_16","alias_value":"CL2IVGRYKVEEKBLQ","created_at":"2026-07-05T06:50:27Z"},{"alias_kind":"pith_short_8","alias_value":"CL2IVGRY","created_at":"2026-07-05T06:50:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:CL2IVGRYKVEEKBLQRX4G4MM2DY","target":"record","payload":{"canonical_record":{"source":{"id":"2210.08126","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2022-10-14T21:46:20Z","cross_cats_sorted":[],"title_canon_sha256":"940aeb476640feb9fad9d2b0a62879c24c5721e17135e9e784e8e8280edf80ec","abstract_canon_sha256":"7e9771700407edbe16a94cc4e6d22ec5f87b59baecbe2fafc610f16dcb224eb6"},"schema_version":"1.0"},"canonical_sha256":"12f48a9a3855484505708df86e319a1e30d14f23f280f9a79d4494595a201df1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:50:27.582573Z","signature_b64":"055IufP3Ly8bB3CjbfI197Vlbl0RubalG07U4Nr9+UuI4raeV76zwFkyv7TSepktoB+1ati6oMb8tTDHRCoKAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"12f48a9a3855484505708df86e319a1e30d14f23f280f9a79d4494595a201df1","last_reissued_at":"2026-07-05T06:50:27.582093Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:50:27.582093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.08126","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:50:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BAlZOg/3nKFldT1Go12g67g21rL+Np+NR6rfls9/5BV6mcNd8Ba+9p9LNUjHbpnMkbmGVyTThGCFFRqXum50Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-22T17:57:28.441920Z"},"content_sha256":"d7cb3ad235218dc0201cfce9e123a37c750f90a2480e41c3b82c77135f1edbaa","schema_version":"1.0","event_id":"sha256:d7cb3ad235218dc0201cfce9e123a37c750f90a2480e41c3b82c77135f1edbaa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:CL2IVGRYKVEEKBLQRX4G4MM2DY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Geometric Reinforcement Learning For Robotic Manipulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Fares J. Abu-Dakka, Hatice Kose, Ibrahim Sevinc, Matteo Saveriano, Naseem Alhousani, Talha Abdulkuddus","submitted_at":"2022-10-14T21:46:20Z","abstract_excerpt":"Reinforcement learning (RL) is a popular technique that allows an agent to learn by trial and error while interacting with a dynamic environment. The traditional Reinforcement Learning (RL) approach has been successful in learning and predicting Euclidean robotic manipulation skills such as positions, velocities, and forces. However, in robotics, it is common to encounter non-Euclidean data such as orientation or stiffness, and failing to account for their geometric nature can negatively impact learning accuracy and performance. In this paper, to address this challenge, we propose a novel fram"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.08126","kind":"arxiv","version":2},"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/2210.08126/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":"2026-07-05T06:50:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x3JxzcSZ7s6T7KVEmlugyiISTnLIS0fPa/yGE+Yl/2EO8oaMIFjOS9Ct+ErSguKsxqmub1TXWg1hIg0lSYSOCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-22T17:57:28.442283Z"},"content_sha256":"a123a44793259ff729d2c6e83815c256f6c4105e093b35d3660540d72d07c9b5","schema_version":"1.0","event_id":"sha256:a123a44793259ff729d2c6e83815c256f6c4105e093b35d3660540d72d07c9b5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CL2IVGRYKVEEKBLQRX4G4MM2DY/bundle.json","state_url":"https://pith.science/pith/CL2IVGRYKVEEKBLQRX4G4MM2DY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CL2IVGRYKVEEKBLQRX4G4MM2DY/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":"2026-07-22T17:57:28Z","links":{"resolver":"https://pith.science/pith/CL2IVGRYKVEEKBLQRX4G4MM2DY","bundle":"https://pith.science/pith/CL2IVGRYKVEEKBLQRX4G4MM2DY/bundle.json","state":"https://pith.science/pith/CL2IVGRYKVEEKBLQRX4G4MM2DY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CL2IVGRYKVEEKBLQRX4G4MM2DY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:CL2IVGRYKVEEKBLQRX4G4MM2DY","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":"7e9771700407edbe16a94cc4e6d22ec5f87b59baecbe2fafc610f16dcb224eb6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2022-10-14T21:46:20Z","title_canon_sha256":"940aeb476640feb9fad9d2b0a62879c24c5721e17135e9e784e8e8280edf80ec"},"schema_version":"1.0","source":{"id":"2210.08126","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.08126","created_at":"2026-07-05T06:50:27Z"},{"alias_kind":"arxiv_version","alias_value":"2210.08126v2","created_at":"2026-07-05T06:50:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.08126","created_at":"2026-07-05T06:50:27Z"},{"alias_kind":"pith_short_12","alias_value":"CL2IVGRYKVEE","created_at":"2026-07-05T06:50:27Z"},{"alias_kind":"pith_short_16","alias_value":"CL2IVGRYKVEEKBLQ","created_at":"2026-07-05T06:50:27Z"},{"alias_kind":"pith_short_8","alias_value":"CL2IVGRY","created_at":"2026-07-05T06:50:27Z"}],"graph_snapshots":[{"event_id":"sha256:a123a44793259ff729d2c6e83815c256f6c4105e093b35d3660540d72d07c9b5","target":"graph","created_at":"2026-07-05T06:50:27Z","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/2210.08126/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Reinforcement learning (RL) is a popular technique that allows an agent to learn by trial and error while interacting with a dynamic environment. The traditional Reinforcement Learning (RL) approach has been successful in learning and predicting Euclidean robotic manipulation skills such as positions, velocities, and forces. However, in robotics, it is common to encounter non-Euclidean data such as orientation or stiffness, and failing to account for their geometric nature can negatively impact learning accuracy and performance. In this paper, to address this challenge, we propose a novel fram","authors_text":"Fares J. Abu-Dakka, Hatice Kose, Ibrahim Sevinc, Matteo Saveriano, Naseem Alhousani, Talha Abdulkuddus","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2022-10-14T21:46:20Z","title":"Geometric Reinforcement Learning For Robotic Manipulation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.08126","kind":"arxiv","version":2},"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:d7cb3ad235218dc0201cfce9e123a37c750f90a2480e41c3b82c77135f1edbaa","target":"record","created_at":"2026-07-05T06:50:27Z","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":"7e9771700407edbe16a94cc4e6d22ec5f87b59baecbe2fafc610f16dcb224eb6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2022-10-14T21:46:20Z","title_canon_sha256":"940aeb476640feb9fad9d2b0a62879c24c5721e17135e9e784e8e8280edf80ec"},"schema_version":"1.0","source":{"id":"2210.08126","kind":"arxiv","version":2}},"canonical_sha256":"12f48a9a3855484505708df86e319a1e30d14f23f280f9a79d4494595a201df1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"12f48a9a3855484505708df86e319a1e30d14f23f280f9a79d4494595a201df1","first_computed_at":"2026-07-05T06:50:27.582093Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:50:27.582093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"055IufP3Ly8bB3CjbfI197Vlbl0RubalG07U4Nr9+UuI4raeV76zwFkyv7TSepktoB+1ati6oMb8tTDHRCoKAg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:50:27.582573Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.08126","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d7cb3ad235218dc0201cfce9e123a37c750f90a2480e41c3b82c77135f1edbaa","sha256:a123a44793259ff729d2c6e83815c256f6c4105e093b35d3660540d72d07c9b5"],"state_sha256":"6dee4779cf855036b8eff8efa2c19d45a9f24c7602539942a094691670c73fe3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/xwjPmUh2gFGqi0w6N/cGZw46udH6FnIVVU0AosyWY7Uwl18gIywybBrxjAcRq6kSxp4PZJmPuFhrXAk5MigCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-22T17:57:28.444639Z","bundle_sha256":"5326565d610772344d6d7652c1f2dd4a0b226857237874158197ae64d378db9a"}}