{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:APMGGOEMDG3SFXPTTPZBS47QMU","short_pith_number":"pith:APMGGOEM","schema_version":"1.0","canonical_sha256":"03d863388c19b722ddf39bf21973f0651bde66ff208f787a9b95c8017994b0bb","source":{"kind":"arxiv","id":"2103.09016","version":2},"attestation_state":"computed","paper":{"title":"Manipulator-Independent Representations for Visual Imitation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Konstantinos Bousmalis, Yusuf Aytar, Yuxiang Zhou","submitted_at":"2021-03-16T12:25:37Z","abstract_excerpt":"Imitation learning is an effective tool for robotic learning tasks where specifying a reinforcement learning (RL) reward is not feasible or where the exploration problem is particularly difficult. Imitation, typically behavior cloning or inverse RL, derive a policy from a collection of first-person action-state trajectories. This is contrary to how humans and other animals imitate: we observe a behavior, even from other species, understand its perceived effect on the state of the environment, and figure out what actions our body can perform to reach a similar outcome. In this work, we explore "},"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":"2103.09016","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-03-16T12:25:37Z","cross_cats_sorted":[],"title_canon_sha256":"e6e9c7f12b27a3ac4c4251fa082d1e1b6436201f19e0c73664841498f0d7a5a3","abstract_canon_sha256":"38af0ff57b19b838251b0158803a16bc952411441fa923579e1b6472cab35d27"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:24:14.138573Z","signature_b64":"t6GzvqrAbv9k//PIEvhTQCzuibdg62AbHroarowZt/r4DSDXsYat+ReOpJJ6Sha8vhjhLI0OuGbrE2851Xy3CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"03d863388c19b722ddf39bf21973f0651bde66ff208f787a9b95c8017994b0bb","last_reissued_at":"2026-07-05T02:24:14.137985Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:24:14.137985Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Manipulator-Independent Representations for Visual Imitation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Konstantinos Bousmalis, Yusuf Aytar, Yuxiang Zhou","submitted_at":"2021-03-16T12:25:37Z","abstract_excerpt":"Imitation learning is an effective tool for robotic learning tasks where specifying a reinforcement learning (RL) reward is not feasible or where the exploration problem is particularly difficult. Imitation, typically behavior cloning or inverse RL, derive a policy from a collection of first-person action-state trajectories. This is contrary to how humans and other animals imitate: we observe a behavior, even from other species, understand its perceived effect on the state of the environment, and figure out what actions our body can perform to reach a similar outcome. In this work, we explore "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.09016","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/2103.09016/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":"2103.09016","created_at":"2026-07-05T02:24:14.138076+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.09016v2","created_at":"2026-07-05T02:24:14.138076+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.09016","created_at":"2026-07-05T02:24:14.138076+00:00"},{"alias_kind":"pith_short_12","alias_value":"APMGGOEMDG3S","created_at":"2026-07-05T02:24:14.138076+00:00"},{"alias_kind":"pith_short_16","alias_value":"APMGGOEMDG3SFXPT","created_at":"2026-07-05T02:24:14.138076+00:00"},{"alias_kind":"pith_short_8","alias_value":"APMGGOEM","created_at":"2026-07-05T02:24:14.138076+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.15483","citing_title":"${\\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities","ref_index":59,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/APMGGOEMDG3SFXPTTPZBS47QMU","json":"https://pith.science/pith/APMGGOEMDG3SFXPTTPZBS47QMU.json","graph_json":"https://pith.science/api/pith-number/APMGGOEMDG3SFXPTTPZBS47QMU/graph.json","events_json":"https://pith.science/api/pith-number/APMGGOEMDG3SFXPTTPZBS47QMU/events.json","paper":"https://pith.science/paper/APMGGOEM"},"agent_actions":{"view_html":"https://pith.science/pith/APMGGOEMDG3SFXPTTPZBS47QMU","download_json":"https://pith.science/pith/APMGGOEMDG3SFXPTTPZBS47QMU.json","view_paper":"https://pith.science/paper/APMGGOEM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.09016&json=true","fetch_graph":"https://pith.science/api/pith-number/APMGGOEMDG3SFXPTTPZBS47QMU/graph.json","fetch_events":"https://pith.science/api/pith-number/APMGGOEMDG3SFXPTTPZBS47QMU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/APMGGOEMDG3SFXPTTPZBS47QMU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/APMGGOEMDG3SFXPTTPZBS47QMU/action/storage_attestation","attest_author":"https://pith.science/pith/APMGGOEMDG3SFXPTTPZBS47QMU/action/author_attestation","sign_citation":"https://pith.science/pith/APMGGOEMDG3SFXPTTPZBS47QMU/action/citation_signature","submit_replication":"https://pith.science/pith/APMGGOEMDG3SFXPTTPZBS47QMU/action/replication_record"}},"created_at":"2026-07-05T02:24:14.138076+00:00","updated_at":"2026-07-05T02:24:14.138076+00:00"}