{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:W7HUX3ZEAPLD5ZGDW3P7DG23WO","short_pith_number":"pith:W7HUX3ZE","canonical_record":{"source":{"id":"2109.13396","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2021-09-27T23:42:12Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"77553db0d2eb8301edb63c8be3ac05770fc7f85bfd9e98af11142622cb1e35e7","abstract_canon_sha256":"d64a0c5131cb832077a8388f095a6403844f9cc0e1337b66058bbb17f28f9942"},"schema_version":"1.0"},"canonical_sha256":"b7cf4bef2403d63ee4c3b6dff19b5bb392960124d2610daa45682ef6293801f8","source":{"kind":"arxiv","id":"2109.13396","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.13396","created_at":"2026-07-05T03:18:02Z"},{"alias_kind":"arxiv_version","alias_value":"2109.13396v1","created_at":"2026-07-05T03:18:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.13396","created_at":"2026-07-05T03:18:02Z"},{"alias_kind":"pith_short_12","alias_value":"W7HUX3ZEAPLD","created_at":"2026-07-05T03:18:02Z"},{"alias_kind":"pith_short_16","alias_value":"W7HUX3ZEAPLD5ZGD","created_at":"2026-07-05T03:18:02Z"},{"alias_kind":"pith_short_8","alias_value":"W7HUX3ZE","created_at":"2026-07-05T03:18:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:W7HUX3ZEAPLD5ZGDW3P7DG23WO","target":"record","payload":{"canonical_record":{"source":{"id":"2109.13396","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2021-09-27T23:42:12Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"77553db0d2eb8301edb63c8be3ac05770fc7f85bfd9e98af11142622cb1e35e7","abstract_canon_sha256":"d64a0c5131cb832077a8388f095a6403844f9cc0e1337b66058bbb17f28f9942"},"schema_version":"1.0"},"canonical_sha256":"b7cf4bef2403d63ee4c3b6dff19b5bb392960124d2610daa45682ef6293801f8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:18:02.297284Z","signature_b64":"knl67scjyyj4tG/skItoF1INeMptQDVPch2tdEtSvb6dg/hiAg47nsvy1O4VUcbGyQjHWs6STFZsz2DYTQFVAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7cf4bef2403d63ee4c3b6dff19b5bb392960124d2610daa45682ef6293801f8","last_reissued_at":"2026-07-05T03:18:02.296857Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:18:02.296857Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.13396","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":"2026-07-05T03:18:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NhNDJmEvSwwvu+UdDjSK6AexR/HM6BqwnglrNqn9m6gS8GDlDgBkgT1ShsSR/dLh6S+XHrQFc4073TXZV+uWCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-22T01:05:58.684338Z"},"content_sha256":"0c1ccb8bed9c7f3fe0c4de65a42bfc386ba24a19be6d1dbb15b8129b586f0ed6","schema_version":"1.0","event_id":"sha256:0c1ccb8bed9c7f3fe0c4de65a42bfc386ba24a19be6d1dbb15b8129b586f0ed6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:W7HUX3ZEAPLD5ZGDW3P7DG23WO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A shared multi-task multi-domain robot dataset doubles success rates for new tasks in new environments when added to just 50 demonstrations.","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Bernadette Bucher, Chelsea Finn, Frederik Ebert, Georgios Georgakis, Karl Schmeckpeper, Kostas Daniilidis, Sergey Levine, Yanlai Yang","submitted_at":"2021-09-27T23:42:12Z","abstract_excerpt":"Robot learning holds the promise of learning policies that generalize broadly. However, such generalization requires sufficiently diverse datasets of the task of interest, which can be prohibitively expensive to collect. In other fields, such as computer vision, it is common to utilize shared, reusable datasets, such as ImageNet, to overcome this challenge, but this has proven difficult in robotics. In this paper, we ask: what would it take to enable practical data reuse in robotics for end-to-end skill learning? We hypothesize that the key is to use datasets with multiple tasks and multiple d"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"jointly training with the proposed dataset and 50 demonstrations of a never-before-seen task in a new domain on average leads to a 2x improvement in success rate compared to using target domain data alone","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the collected tasks and domains are representative enough that cross-domain data produces positive transfer rather than interference for arbitrary new tasks and environments.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A large multi-task multi-domain robot dataset combined with 50 new demonstrations yields 2x higher success rates on never-before-seen tasks in new domains.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A shared multi-task multi-domain robot dataset doubles success rates for new tasks in new environments when added to just 50 demonstrations.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"3a32b3447f97e7f0931f9c13d452d3333f56fac78f8b59aec215d9380a7dd31b"},"source":{"id":"2109.13396","kind":"arxiv","version":1},"verdict":{"id":"0f2e11a0-3517-4054-be91-9a38f96cd876","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-13T19:51:16.787575Z","strongest_claim":"jointly training with the proposed dataset and 50 demonstrations of a never-before-seen task in a new domain on average leads to a 2x improvement in success rate compared to using target domain data alone","one_line_summary":"A large multi-task multi-domain robot dataset combined with 50 new demonstrations yields 2x higher success rates on never-before-seen tasks in new domains.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the collected tasks and domains are representative enough that cross-domain data produces positive transfer rather than interference for arbitrary new tasks and environments.","pith_extraction_headline":"A shared multi-task multi-domain robot dataset doubles success rates for new tasks in new environments when added to just 50 demonstrations."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2109.13396/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":28,"sample":[{"doi":"","year":2012,"title":"Imagenet classiﬁca- tion with deep convolutional neural networks","work_id":"08ad5a97-cec1-4b1a-8d2f-af4f03c45e08","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2018,"title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","work_id":"ed240a10-5b19-406c-baa5-30803f465785","ref_index":2,"cited_arxiv_id":"1810.04805","is_internal_anchor":true},{"doi":"","year":2009,"title":"Imagenet: A large-scale hierarchical image database","work_id":"78bbc043-c6d8-4572-b5d6-54eaf5a89fb1","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2001,"title":"Gradient surgery for multi-task learning","work_id":"044e1624-6f75-4f7f-8757-624618e6015f","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Mt-opt: Continuous multi-task robotic reinforcement learning at scale","work_id":"d4b61039-1d94-42db-8c6d-4c49c037e711","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":28,"snapshot_sha256":"350c1b7364023aef85c04f3a64a377c8bdf7a170f84e4c775899e0462a1d4f17","internal_anchors":2},"formal_canon":{"evidence_count":2,"snapshot_sha256":"eea2b2d019169268c3b3d6a9282b6cda74336b83d4913c4c1c8778c1d438f98f"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"0f2e11a0-3517-4054-be91-9a38f96cd876"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:18:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cTiDNAczi6BWPykTwvhymIt87+oCKSW3XzNz8buMCRlWvs3kMNB2ow13WUMLu6q6UwvGYQ4QUUq7KMPMW38UCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-22T01:05:58.684885Z"},"content_sha256":"23a008d39a5e15040c30a95ad2e2deafa722e087d6e7883cc86f7cf857bd694e","schema_version":"1.0","event_id":"sha256:23a008d39a5e15040c30a95ad2e2deafa722e087d6e7883cc86f7cf857bd694e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/W7HUX3ZEAPLD5ZGDW3P7DG23WO/bundle.json","state_url":"https://pith.science/pith/W7HUX3ZEAPLD5ZGDW3P7DG23WO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/W7HUX3ZEAPLD5ZGDW3P7DG23WO/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":"2026-07-22T01:05:58Z","links":{"resolver":"https://pith.science/pith/W7HUX3ZEAPLD5ZGDW3P7DG23WO","bundle":"https://pith.science/pith/W7HUX3ZEAPLD5ZGDW3P7DG23WO/bundle.json","state":"https://pith.science/pith/W7HUX3ZEAPLD5ZGDW3P7DG23WO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/W7HUX3ZEAPLD5ZGDW3P7DG23WO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:W7HUX3ZEAPLD5ZGDW3P7DG23WO","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":"d64a0c5131cb832077a8388f095a6403844f9cc0e1337b66058bbb17f28f9942","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2021-09-27T23:42:12Z","title_canon_sha256":"77553db0d2eb8301edb63c8be3ac05770fc7f85bfd9e98af11142622cb1e35e7"},"schema_version":"1.0","source":{"id":"2109.13396","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.13396","created_at":"2026-07-05T03:18:02Z"},{"alias_kind":"arxiv_version","alias_value":"2109.13396v1","created_at":"2026-07-05T03:18:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.13396","created_at":"2026-07-05T03:18:02Z"},{"alias_kind":"pith_short_12","alias_value":"W7HUX3ZEAPLD","created_at":"2026-07-05T03:18:02Z"},{"alias_kind":"pith_short_16","alias_value":"W7HUX3ZEAPLD5ZGD","created_at":"2026-07-05T03:18:02Z"},{"alias_kind":"pith_short_8","alias_value":"W7HUX3ZE","created_at":"2026-07-05T03:18:02Z"}],"graph_snapshots":[{"event_id":"sha256:23a008d39a5e15040c30a95ad2e2deafa722e087d6e7883cc86f7cf857bd694e","target":"graph","created_at":"2026-07-05T03:18:02Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"jointly training with the proposed dataset and 50 demonstrations of a never-before-seen task in a new domain on average leads to a 2x improvement in success rate compared to using target domain data alone"},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the collected tasks and domains are representative enough that cross-domain data produces positive transfer rather than interference for arbitrary new tasks and environments."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A large multi-task multi-domain robot dataset combined with 50 new demonstrations yields 2x higher success rates on never-before-seen tasks in new domains."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A shared multi-task multi-domain robot dataset doubles success rates for new tasks in new environments when added to just 50 demonstrations."}],"snapshot_sha256":"3a32b3447f97e7f0931f9c13d452d3333f56fac78f8b59aec215d9380a7dd31b"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"eea2b2d019169268c3b3d6a9282b6cda74336b83d4913c4c1c8778c1d438f98f"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2109.13396/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Robot learning holds the promise of learning policies that generalize broadly. However, such generalization requires sufficiently diverse datasets of the task of interest, which can be prohibitively expensive to collect. In other fields, such as computer vision, it is common to utilize shared, reusable datasets, such as ImageNet, to overcome this challenge, but this has proven difficult in robotics. In this paper, we ask: what would it take to enable practical data reuse in robotics for end-to-end skill learning? We hypothesize that the key is to use datasets with multiple tasks and multiple d","authors_text":"Bernadette Bucher, Chelsea Finn, Frederik Ebert, Georgios Georgakis, Karl Schmeckpeper, Kostas Daniilidis, Sergey Levine, Yanlai Yang","cross_cats":["cs.AI"],"headline":"A shared multi-task multi-domain robot dataset doubles success rates for new tasks in new environments when added to just 50 demonstrations.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2021-09-27T23:42:12Z","title":"Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets"},"references":{"count":28,"internal_anchors":2,"resolved_work":28,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Imagenet classiﬁca- tion with deep convolutional neural networks","work_id":"08ad5a97-cec1-4b1a-8d2f-af4f03c45e08","year":2012},{"cited_arxiv_id":"1810.04805","doi":"","is_internal_anchor":true,"ref_index":2,"title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","work_id":"ed240a10-5b19-406c-baa5-30803f465785","year":2018},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Imagenet: A large-scale hierarchical image database","work_id":"78bbc043-c6d8-4572-b5d6-54eaf5a89fb1","year":2009},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Gradient surgery for multi-task learning","work_id":"044e1624-6f75-4f7f-8757-624618e6015f","year":2001},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Mt-opt: Continuous multi-task robotic reinforcement learning at scale","work_id":"d4b61039-1d94-42db-8c6d-4c49c037e711","year":2021}],"snapshot_sha256":"350c1b7364023aef85c04f3a64a377c8bdf7a170f84e4c775899e0462a1d4f17"},"source":{"id":"2109.13396","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-13T19:51:16.787575Z","id":"0f2e11a0-3517-4054-be91-9a38f96cd876","model_set":{"reader":"grok-4.3"},"one_line_summary":"A large multi-task multi-domain robot dataset combined with 50 new demonstrations yields 2x higher success rates on never-before-seen tasks in new domains.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A shared multi-task multi-domain robot dataset doubles success rates for new tasks in new environments when added to just 50 demonstrations.","strongest_claim":"jointly training with the proposed dataset and 50 demonstrations of a never-before-seen task in a new domain on average leads to a 2x improvement in success rate compared to using target domain data alone","weakest_assumption":"That the collected tasks and domains are representative enough that cross-domain data produces positive transfer rather than interference for arbitrary new tasks and environments."}},"verdict_id":"0f2e11a0-3517-4054-be91-9a38f96cd876"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:0c1ccb8bed9c7f3fe0c4de65a42bfc386ba24a19be6d1dbb15b8129b586f0ed6","target":"record","created_at":"2026-07-05T03:18:02Z","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":"d64a0c5131cb832077a8388f095a6403844f9cc0e1337b66058bbb17f28f9942","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2021-09-27T23:42:12Z","title_canon_sha256":"77553db0d2eb8301edb63c8be3ac05770fc7f85bfd9e98af11142622cb1e35e7"},"schema_version":"1.0","source":{"id":"2109.13396","kind":"arxiv","version":1}},"canonical_sha256":"b7cf4bef2403d63ee4c3b6dff19b5bb392960124d2610daa45682ef6293801f8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b7cf4bef2403d63ee4c3b6dff19b5bb392960124d2610daa45682ef6293801f8","first_computed_at":"2026-07-05T03:18:02.296857Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:18:02.296857Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"knl67scjyyj4tG/skItoF1INeMptQDVPch2tdEtSvb6dg/hiAg47nsvy1O4VUcbGyQjHWs6STFZsz2DYTQFVAA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:18:02.297284Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.13396","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0c1ccb8bed9c7f3fe0c4de65a42bfc386ba24a19be6d1dbb15b8129b586f0ed6","sha256:23a008d39a5e15040c30a95ad2e2deafa722e087d6e7883cc86f7cf857bd694e"],"state_sha256":"aee924e3065ceba2592e768a98bfde1a5996edab54976ea74ddc3875d8fd66b3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wMOBqkaXNC1izHwElevvkfOizzbdwtpbgHYaerADk+Et3tw0saj2yclUcKffwIDEXo9Oq400auaugN1H0GzFDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-22T01:05:58.687419Z","bundle_sha256":"562f88f709b371e6544477a514945e606bdf7df77c1f898377124c283b402630"}}