{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MS7XGRLSXEP3KM2WLZQUAJU7PB","short_pith_number":"pith:MS7XGRLS","schema_version":"1.0","canonical_sha256":"64bf734572b91fb533565e6140269f787b2f84b5c9b7f1ffd2b73488243faa58","source":{"kind":"arxiv","id":"2505.03296","version":2},"attestation_state":"computed","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"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"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"},"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"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2505.03296","created_at":"2026-06-11T01:09:12.787244+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.03296v2","created_at":"2026-06-11T01:09:12.787244+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.03296","created_at":"2026-06-11T01:09:12.787244+00:00"},{"alias_kind":"pith_short_12","alias_value":"MS7XGRLSXEP3","created_at":"2026-06-11T01:09:12.787244+00:00"},{"alias_kind":"pith_short_16","alias_value":"MS7XGRLSXEP3KM2W","created_at":"2026-06-11T01:09:12.787244+00:00"},{"alias_kind":"pith_short_8","alias_value":"MS7XGRLS","created_at":"2026-06-11T01:09:12.787244+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2606.09758","citing_title":"Difference-Aware Retrieval Policies for Imitation Learning","ref_index":35,"is_internal_anchor":true},{"citing_arxiv_id":"2606.09758","citing_title":"Difference-Aware Retrieval Policies for Imitation Learning","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MS7XGRLSXEP3KM2WLZQUAJU7PB","json":"https://pith.science/pith/MS7XGRLSXEP3KM2WLZQUAJU7PB.json","graph_json":"https://pith.science/api/pith-number/MS7XGRLSXEP3KM2WLZQUAJU7PB/graph.json","events_json":"https://pith.science/api/pith-number/MS7XGRLSXEP3KM2WLZQUAJU7PB/events.json","paper":"https://pith.science/paper/MS7XGRLS"},"agent_actions":{"view_html":"https://pith.science/pith/MS7XGRLSXEP3KM2WLZQUAJU7PB","download_json":"https://pith.science/pith/MS7XGRLSXEP3KM2WLZQUAJU7PB.json","view_paper":"https://pith.science/paper/MS7XGRLS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.03296&json=true","fetch_graph":"https://pith.science/api/pith-number/MS7XGRLSXEP3KM2WLZQUAJU7PB/graph.json","fetch_events":"https://pith.science/api/pith-number/MS7XGRLSXEP3KM2WLZQUAJU7PB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MS7XGRLSXEP3KM2WLZQUAJU7PB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MS7XGRLSXEP3KM2WLZQUAJU7PB/action/storage_attestation","attest_author":"https://pith.science/pith/MS7XGRLSXEP3KM2WLZQUAJU7PB/action/author_attestation","sign_citation":"https://pith.science/pith/MS7XGRLSXEP3KM2WLZQUAJU7PB/action/citation_signature","submit_replication":"https://pith.science/pith/MS7XGRLSXEP3KM2WLZQUAJU7PB/action/replication_record"}},"created_at":"2026-06-11T01:09:12.787244+00:00","updated_at":"2026-06-11T01:09:12.787244+00:00"}