{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:HC5T3BQOOZXWQLQAEEMQUR5DZ4","short_pith_number":"pith:HC5T3BQO","schema_version":"1.0","canonical_sha256":"38bb3d860e766f682e0021190a47a3cf276d2c795e0fbecb4db1b8620fca0e3a","source":{"kind":"arxiv","id":"2103.05079","version":1},"attestation_state":"computed","paper":{"title":"Domain-Robust Visual Imitation Learning with Mutual Information Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Edoardo Cetin, Oya Celiktutan","submitted_at":"2021-03-08T21:18:58Z","abstract_excerpt":"Human beings are able to understand objectives and learn by simply observing others perform a task. Imitation learning methods aim to replicate such capabilities, however, they generally depend on access to a full set of optimal states and actions taken with the agent's actuators and from the agent's point of view. In this paper, we introduce a new algorithm - called Disentangling Generative Adversarial Imitation Learning (DisentanGAIL) - with the purpose of bypassing such constraints. Our algorithm enables autonomous agents to learn directly from high dimensional observations of an expert per"},"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":"2103.05079","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-08T21:18:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b2e645601b202af7c1bad2badd9351aab628a257842aa4cf4a837761d0ddf49b","abstract_canon_sha256":"09befe511a9aa2c9089fee415797dc07dc2c79b5aa393e4f9f6bc784bddbdc97"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:21:21.934991Z","signature_b64":"XpAWUtSEYSpjVYe4/x6JF5z0VPIyY0dUwNOI7DJM6J71MeAL4umMKgFfaIVNLkj7QsVbgMw+9xbpuR6PjvuJDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38bb3d860e766f682e0021190a47a3cf276d2c795e0fbecb4db1b8620fca0e3a","last_reissued_at":"2026-07-05T02:21:21.934521Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:21:21.934521Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Domain-Robust Visual Imitation Learning with Mutual Information Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Edoardo Cetin, Oya Celiktutan","submitted_at":"2021-03-08T21:18:58Z","abstract_excerpt":"Human beings are able to understand objectives and learn by simply observing others perform a task. Imitation learning methods aim to replicate such capabilities, however, they generally depend on access to a full set of optimal states and actions taken with the agent's actuators and from the agent's point of view. In this paper, we introduce a new algorithm - called Disentangling Generative Adversarial Imitation Learning (DisentanGAIL) - with the purpose of bypassing such constraints. Our algorithm enables autonomous agents to learn directly from high dimensional observations of an expert per"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.05079","kind":"arxiv","version":1},"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/2103.05079/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":"2103.05079","created_at":"2026-07-05T02:21:21.934587+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.05079v1","created_at":"2026-07-05T02:21:21.934587+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.05079","created_at":"2026-07-05T02:21:21.934587+00:00"},{"alias_kind":"pith_short_12","alias_value":"HC5T3BQOOZXW","created_at":"2026-07-05T02:21:21.934587+00:00"},{"alias_kind":"pith_short_16","alias_value":"HC5T3BQOOZXWQLQA","created_at":"2026-07-05T02:21:21.934587+00:00"},{"alias_kind":"pith_short_8","alias_value":"HC5T3BQO","created_at":"2026-07-05T02:21:21.934587+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.03201","citing_title":"Reinforcement Learning from Cross-domain Videos with Video Prediction Model","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HC5T3BQOOZXWQLQAEEMQUR5DZ4","json":"https://pith.science/pith/HC5T3BQOOZXWQLQAEEMQUR5DZ4.json","graph_json":"https://pith.science/api/pith-number/HC5T3BQOOZXWQLQAEEMQUR5DZ4/graph.json","events_json":"https://pith.science/api/pith-number/HC5T3BQOOZXWQLQAEEMQUR5DZ4/events.json","paper":"https://pith.science/paper/HC5T3BQO"},"agent_actions":{"view_html":"https://pith.science/pith/HC5T3BQOOZXWQLQAEEMQUR5DZ4","download_json":"https://pith.science/pith/HC5T3BQOOZXWQLQAEEMQUR5DZ4.json","view_paper":"https://pith.science/paper/HC5T3BQO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.05079&json=true","fetch_graph":"https://pith.science/api/pith-number/HC5T3BQOOZXWQLQAEEMQUR5DZ4/graph.json","fetch_events":"https://pith.science/api/pith-number/HC5T3BQOOZXWQLQAEEMQUR5DZ4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HC5T3BQOOZXWQLQAEEMQUR5DZ4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HC5T3BQOOZXWQLQAEEMQUR5DZ4/action/storage_attestation","attest_author":"https://pith.science/pith/HC5T3BQOOZXWQLQAEEMQUR5DZ4/action/author_attestation","sign_citation":"https://pith.science/pith/HC5T3BQOOZXWQLQAEEMQUR5DZ4/action/citation_signature","submit_replication":"https://pith.science/pith/HC5T3BQOOZXWQLQAEEMQUR5DZ4/action/replication_record"}},"created_at":"2026-07-05T02:21:21.934587+00:00","updated_at":"2026-07-05T02:21:21.934587+00:00"}