{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:AYDA6YABRZJGRKTE3QWKLQBAHF","short_pith_number":"pith:AYDA6YAB","canonical_record":{"source":{"id":"2502.12371","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-02-17T23:22:49Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"6b122b75e401f72aafae70036fe048135d57a36108078b29626502e4cd8338d5","abstract_canon_sha256":"0b4431ab2e0f76dcd3974fd0a457236351011c54e5ad24423b390eb72b801e4f"},"schema_version":"1.0"},"canonical_sha256":"06060f60018e5268aa64dc2ca5c02039681e621db04b56aa719a56044a8a7718","source":{"kind":"arxiv","id":"2502.12371","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.12371","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"arxiv_version","alias_value":"2502.12371v2","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.12371","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"pith_short_12","alias_value":"AYDA6YABRZJG","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"pith_short_16","alias_value":"AYDA6YABRZJGRKTE","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"pith_short_8","alias_value":"AYDA6YAB","created_at":"2026-07-05T10:28:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:AYDA6YABRZJGRKTE3QWKLQBAHF","target":"record","payload":{"canonical_record":{"source":{"id":"2502.12371","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-02-17T23:22:49Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"6b122b75e401f72aafae70036fe048135d57a36108078b29626502e4cd8338d5","abstract_canon_sha256":"0b4431ab2e0f76dcd3974fd0a457236351011c54e5ad24423b390eb72b801e4f"},"schema_version":"1.0"},"canonical_sha256":"06060f60018e5268aa64dc2ca5c02039681e621db04b56aa719a56044a8a7718","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:28:36.350732Z","signature_b64":"x+D6q5Hr468Lsl4WPZpQJs7ca8lO7kjRb2ehuQbwec3PtKvA2Qg7pXTZAdMrBGlpROGHvcPvqirawKJEmsR/BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"06060f60018e5268aa64dc2ca5c02039681e621db04b56aa719a56044a8a7718","last_reissued_at":"2026-07-05T10:28:36.349734Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:28:36.349734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.12371","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":"2026-07-05T10:28:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EObiPCDDEZU07Ihgof0nxC0hW4SLRdZK5Uy3UfUrQX8HBg1+j9usk7JbvYg6IePjj1DWEMQX9+CbLPzhSOqMDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T00:44:31.526071Z"},"content_sha256":"88fdd5a79248c2d85ccb865c0d188c1f02b1ec87d011c3d2511e730535aec78b","schema_version":"1.0","event_id":"sha256:88fdd5a79248c2d85ccb865c0d188c1f02b1ec87d011c3d2511e730535aec78b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:AYDA6YABRZJGRKTE3QWKLQBAHF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"IMLE Policy: Fast and Sample Efficient Visuomotor Policy Learning via Implicit Maximum Likelihood Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.RO","authors_text":"David Pershouse, Krishan Rana, Niko Suenderhauf, Robert Lee","submitted_at":"2025-02-17T23:22:49Z","abstract_excerpt":"Recent advances in imitation learning, particularly using generative modelling techniques like diffusion, have enabled policies to capture complex multi-modal action distributions. However, these methods often require large datasets and multiple inference steps for action generation, posing challenges in robotics where the cost for data collection is high and computation resources are limited. To address this, we introduce IMLE Policy, a novel behaviour cloning approach based on Implicit Maximum Likelihood Estimation (IMLE). IMLE Policy excels in low-data regimes, effectively learning from min"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.12371","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/2502.12371/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":"2026-07-05T10:28:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lS84l68GhkyNSiWjccbyf9Bj1QySJcTJYqAG8ONnr/mDzD5lw8+4o7yrYtTr/Hu8SUkUuNpIET7pSlEhZKIFAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T00:44:31.526584Z"},"content_sha256":"542b2c5241e54e54bcd6b995a7034a0ce81850054979930fe951dd4d454a9fbc","schema_version":"1.0","event_id":"sha256:542b2c5241e54e54bcd6b995a7034a0ce81850054979930fe951dd4d454a9fbc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AYDA6YABRZJGRKTE3QWKLQBAHF/bundle.json","state_url":"https://pith.science/pith/AYDA6YABRZJGRKTE3QWKLQBAHF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AYDA6YABRZJGRKTE3QWKLQBAHF/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":"2026-08-09T00:44:31Z","links":{"resolver":"https://pith.science/pith/AYDA6YABRZJGRKTE3QWKLQBAHF","bundle":"https://pith.science/pith/AYDA6YABRZJGRKTE3QWKLQBAHF/bundle.json","state":"https://pith.science/pith/AYDA6YABRZJGRKTE3QWKLQBAHF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AYDA6YABRZJGRKTE3QWKLQBAHF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:AYDA6YABRZJGRKTE3QWKLQBAHF","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":"0b4431ab2e0f76dcd3974fd0a457236351011c54e5ad24423b390eb72b801e4f","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-02-17T23:22:49Z","title_canon_sha256":"6b122b75e401f72aafae70036fe048135d57a36108078b29626502e4cd8338d5"},"schema_version":"1.0","source":{"id":"2502.12371","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.12371","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"arxiv_version","alias_value":"2502.12371v2","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.12371","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"pith_short_12","alias_value":"AYDA6YABRZJG","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"pith_short_16","alias_value":"AYDA6YABRZJGRKTE","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"pith_short_8","alias_value":"AYDA6YAB","created_at":"2026-07-05T10:28:36Z"}],"graph_snapshots":[{"event_id":"sha256:542b2c5241e54e54bcd6b995a7034a0ce81850054979930fe951dd4d454a9fbc","target":"graph","created_at":"2026-07-05T10:28:36Z","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/2502.12371/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in imitation learning, particularly using generative modelling techniques like diffusion, have enabled policies to capture complex multi-modal action distributions. However, these methods often require large datasets and multiple inference steps for action generation, posing challenges in robotics where the cost for data collection is high and computation resources are limited. To address this, we introduce IMLE Policy, a novel behaviour cloning approach based on Implicit Maximum Likelihood Estimation (IMLE). IMLE Policy excels in low-data regimes, effectively learning from min","authors_text":"David Pershouse, Krishan Rana, Niko Suenderhauf, Robert Lee","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-02-17T23:22:49Z","title":"IMLE Policy: Fast and Sample Efficient Visuomotor Policy Learning via Implicit Maximum Likelihood Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.12371","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":"sha256:88fdd5a79248c2d85ccb865c0d188c1f02b1ec87d011c3d2511e730535aec78b","target":"record","created_at":"2026-07-05T10:28:36Z","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":"0b4431ab2e0f76dcd3974fd0a457236351011c54e5ad24423b390eb72b801e4f","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-02-17T23:22:49Z","title_canon_sha256":"6b122b75e401f72aafae70036fe048135d57a36108078b29626502e4cd8338d5"},"schema_version":"1.0","source":{"id":"2502.12371","kind":"arxiv","version":2}},"canonical_sha256":"06060f60018e5268aa64dc2ca5c02039681e621db04b56aa719a56044a8a7718","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"06060f60018e5268aa64dc2ca5c02039681e621db04b56aa719a56044a8a7718","first_computed_at":"2026-07-05T10:28:36.349734Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:28:36.349734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"x+D6q5Hr468Lsl4WPZpQJs7ca8lO7kjRb2ehuQbwec3PtKvA2Qg7pXTZAdMrBGlpROGHvcPvqirawKJEmsR/BA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:28:36.350732Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.12371","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:88fdd5a79248c2d85ccb865c0d188c1f02b1ec87d011c3d2511e730535aec78b","sha256:542b2c5241e54e54bcd6b995a7034a0ce81850054979930fe951dd4d454a9fbc"],"state_sha256":"aff6e298fb4390013e96f3966c0426a2f390162b19a9d593f5cb4ab5e6f67a35"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IrFA/ec/TQMBP5Ag0VRJojrzj1iNM/zrkFGGjPtja+sW9T+4jQyFlM7iUWr5fYTX+uXVGXPKLwQ+StS9taxaAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T00:44:31.531269Z","bundle_sha256":"28904a48c378ba92fa42961a652c804c21b3e4f2c1cf2030c54123efdad98076"}}