{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:MS7XGRLSXEP3KM2WLZQUAJU7PB","short_pith_number":"pith:MS7XGRLS","canonical_record":{"source":{"id":"2505.03296","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-05-06T08:27:23Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"3d1af185de506c0377709c3bbfd46972378f2c62d30e0929ea3aae6872324c18","abstract_canon_sha256":"18b3b6dd541f2766995a86874bba4eaee8f08aa4b7678bfdb4a97faca53b338a"},"schema_version":"1.0"},"canonical_sha256":"64bf734572b91fb533565e6140269f787b2f84b5c9b7f1ffd2b73488243faa58","source":{"kind":"arxiv","id":"2505.03296","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.03296","created_at":"2026-06-11T01:09:12Z"},{"alias_kind":"arxiv_version","alias_value":"2505.03296v2","created_at":"2026-06-11T01:09:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.03296","created_at":"2026-06-11T01:09:12Z"},{"alias_kind":"pith_short_12","alias_value":"MS7XGRLSXEP3","created_at":"2026-06-11T01:09:12Z"},{"alias_kind":"pith_short_16","alias_value":"MS7XGRLSXEP3KM2W","created_at":"2026-06-11T01:09:12Z"},{"alias_kind":"pith_short_8","alias_value":"MS7XGRLS","created_at":"2026-06-11T01:09:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:MS7XGRLSXEP3KM2WLZQUAJU7PB","target":"record","payload":{"canonical_record":{"source":{"id":"2505.03296","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-05-06T08:27:23Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"3d1af185de506c0377709c3bbfd46972378f2c62d30e0929ea3aae6872324c18","abstract_canon_sha256":"18b3b6dd541f2766995a86874bba4eaee8f08aa4b7678bfdb4a97faca53b338a"},"schema_version":"1.0"},"canonical_sha256":"64bf734572b91fb533565e6140269f787b2f84b5c9b7f1ffd2b73488243faa58","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-11T01:09:12.787911Z","signature_b64":"i2l7cECa851Jr5rMQpb7ekjrSA6s2aS7qqIlqXiZstVk/bBpk3Vw0Bk+BETqguBgMklSwfkO+h6r0qvvSvhYCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64bf734572b91fb533565e6140269f787b2f84b5c9b7f1ffd2b73488243faa58","last_reissued_at":"2026-06-11T01:09:12.787171Z","signature_status":"signed_v1","first_computed_at":"2026-06-11T01:09:12.787171Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.03296","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":"2026-06-11T01:09:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nQQO4mGBQRQKgiyK2v3NyrSUI5ShhtQA9vqPGuDNQgAJjV3gkvznQGSOsH8PyKACIBU0Z33IdvGJ50uYYce1Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T07:09:14.150524Z"},"content_sha256":"dc432db2440d20116e7b6f6dbfac80ac898c709ce40b7a97cb4c82d2ddc2355c","schema_version":"1.0","event_id":"sha256:dc432db2440d20116e7b6f6dbfac80ac898c709ce40b7a97cb4c82d2ddc2355c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:MS7XGRLSXEP3KM2WLZQUAJU7PB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.RO","authors_text":"Abhinav Valada, Adrian R\\\"ofer, Jan Ole von Hartz, Joschka Boedecker","submitted_at":"2025-05-06T08:27:23Z","abstract_excerpt":"We present Mixture of Discrete-time Gaussian Processes (MiDiGap), a novel approach for flexible policy representation and imitation learning in robot manipulation. MiDiGap enables learning from as few as five demonstrations using only camera observations and generalizes across a wide range of challenging tasks. It excels at long-horizon behaviors such as making coffee, highly constrained motions such as opening doors, dynamic actions such as scooping with a spatula, and multimodal tasks such as hanging a mug. MiDiGap learns these tasks on a CPU in less than a minute and scales linearly to larg"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.03296","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/2505.03296/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":"2026-06-11T01:09:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qeWa8S/jZx2Jfhq7QcaFxuWFM1vBb0cxJ+zIuzZeP/1Yc+h5jVjYxOn6xs7CQU5D0PXpAFF57We5jSkE+kdFBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T07:09:14.150908Z"},"content_sha256":"ccd77e8c6f3321734c7fce8d0f8e948674c544536ea51ec82afa1f797497e357","schema_version":"1.0","event_id":"sha256:ccd77e8c6f3321734c7fce8d0f8e948674c544536ea51ec82afa1f797497e357"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MS7XGRLSXEP3KM2WLZQUAJU7PB/bundle.json","state_url":"https://pith.science/pith/MS7XGRLSXEP3KM2WLZQUAJU7PB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MS7XGRLSXEP3KM2WLZQUAJU7PB/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":"2026-07-03T07:09:14Z","links":{"resolver":"https://pith.science/pith/MS7XGRLSXEP3KM2WLZQUAJU7PB","bundle":"https://pith.science/pith/MS7XGRLSXEP3KM2WLZQUAJU7PB/bundle.json","state":"https://pith.science/pith/MS7XGRLSXEP3KM2WLZQUAJU7PB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MS7XGRLSXEP3KM2WLZQUAJU7PB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:MS7XGRLSXEP3KM2WLZQUAJU7PB","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":"18b3b6dd541f2766995a86874bba4eaee8f08aa4b7678bfdb4a97faca53b338a","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-05-06T08:27:23Z","title_canon_sha256":"3d1af185de506c0377709c3bbfd46972378f2c62d30e0929ea3aae6872324c18"},"schema_version":"1.0","source":{"id":"2505.03296","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.03296","created_at":"2026-06-11T01:09:12Z"},{"alias_kind":"arxiv_version","alias_value":"2505.03296v2","created_at":"2026-06-11T01:09:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.03296","created_at":"2026-06-11T01:09:12Z"},{"alias_kind":"pith_short_12","alias_value":"MS7XGRLSXEP3","created_at":"2026-06-11T01:09:12Z"},{"alias_kind":"pith_short_16","alias_value":"MS7XGRLSXEP3KM2W","created_at":"2026-06-11T01:09:12Z"},{"alias_kind":"pith_short_8","alias_value":"MS7XGRLS","created_at":"2026-06-11T01:09:12Z"}],"graph_snapshots":[{"event_id":"sha256:ccd77e8c6f3321734c7fce8d0f8e948674c544536ea51ec82afa1f797497e357","target":"graph","created_at":"2026-06-11T01:09:12Z","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/2505.03296/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present Mixture of Discrete-time Gaussian Processes (MiDiGap), a novel approach for flexible policy representation and imitation learning in robot manipulation. MiDiGap enables learning from as few as five demonstrations using only camera observations and generalizes across a wide range of challenging tasks. It excels at long-horizon behaviors such as making coffee, highly constrained motions such as opening doors, dynamic actions such as scooping with a spatula, and multimodal tasks such as hanging a mug. MiDiGap learns these tasks on a CPU in less than a minute and scales linearly to larg","authors_text":"Abhinav Valada, Adrian R\\\"ofer, Jan Ole von Hartz, Joschka Boedecker","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-05-06T08:27:23Z","title":"The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.03296","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:dc432db2440d20116e7b6f6dbfac80ac898c709ce40b7a97cb4c82d2ddc2355c","target":"record","created_at":"2026-06-11T01:09:12Z","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":"18b3b6dd541f2766995a86874bba4eaee8f08aa4b7678bfdb4a97faca53b338a","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-05-06T08:27:23Z","title_canon_sha256":"3d1af185de506c0377709c3bbfd46972378f2c62d30e0929ea3aae6872324c18"},"schema_version":"1.0","source":{"id":"2505.03296","kind":"arxiv","version":2}},"canonical_sha256":"64bf734572b91fb533565e6140269f787b2f84b5c9b7f1ffd2b73488243faa58","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"64bf734572b91fb533565e6140269f787b2f84b5c9b7f1ffd2b73488243faa58","first_computed_at":"2026-06-11T01:09:12.787171Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-11T01:09:12.787171Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"i2l7cECa851Jr5rMQpb7ekjrSA6s2aS7qqIlqXiZstVk/bBpk3Vw0Bk+BETqguBgMklSwfkO+h6r0qvvSvhYCg==","signature_status":"signed_v1","signed_at":"2026-06-11T01:09:12.787911Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.03296","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dc432db2440d20116e7b6f6dbfac80ac898c709ce40b7a97cb4c82d2ddc2355c","sha256:ccd77e8c6f3321734c7fce8d0f8e948674c544536ea51ec82afa1f797497e357"],"state_sha256":"a64da7fea43104db37bb9fce13c305730d75b2a39ba283949fa5a67678eae56f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9zQcRjlThW4oX05StgIYRSMRns9Y6xs7mMf3S8niwv/Vs2DGhVhUGn0s1iCcWs1BRiDO5G/05jGa5z42xypiDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T07:09:14.153007Z","bundle_sha256":"adfb00a02e7dd58a5f17674e0518267724152067856d6468e6eb573fdbbe9ecb"}}