{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:QNXVDRPV6TVKFIBMZXHNRT4U6K","short_pith_number":"pith:QNXVDRPV","schema_version":"1.0","canonical_sha256":"836f51c5f5f4eaa2a02ccdced8cf94f2a0e14ffe17bf475b71381c2a09b059a0","source":{"kind":"arxiv","id":"1703.06907","version":1},"attestation_state":"computed","paper":{"title":"Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Alex Ray, Jonas Schneider, Josh Tobin, Pieter Abbeel, Rachel Fong, Wojciech Zaremba","submitted_at":"2017-03-20T18:17:25Z","abstract_excerpt":"Bridging the 'reality gap' that separates simulated robotics from experiments on hardware could accelerate robotic research through improved data availability. This paper explores domain randomization, a simple technique for training models on simulated images that transfer to real images by randomizing rendering in the simulator. With enough variability in the simulator, the real world may appear to the model as just another variation. We focus on the task of object localization, which is a stepping stone to general robotic manipulation skills. We find that it is possible to train a real-worl"},"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":"1703.06907","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-03-20T18:17:25Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5d38e8f7979b30da1050c3ba41a4fb1228422c3a21f7673a620c50b42d7ddc0f","abstract_canon_sha256":"d1e0aa427165d4bb633f1d840cfd1de0637e00266d1732a05fcd989d5b5b673e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:48:13.969288Z","signature_b64":"C/SjUH9qQr/mbML8kSoh8Kudte5AOnRAowOPvzgPt5XEjm/uTFaGA5rRsm4GLvqOFaU6++qwfzw/h3KL9a7HDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"836f51c5f5f4eaa2a02ccdced8cf94f2a0e14ffe17bf475b71381c2a09b059a0","last_reissued_at":"2026-05-18T00:48:13.968586Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:48:13.968586Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Alex Ray, Jonas Schneider, Josh Tobin, Pieter Abbeel, Rachel Fong, Wojciech Zaremba","submitted_at":"2017-03-20T18:17:25Z","abstract_excerpt":"Bridging the 'reality gap' that separates simulated robotics from experiments on hardware could accelerate robotic research through improved data availability. This paper explores domain randomization, a simple technique for training models on simulated images that transfer to real images by randomizing rendering in the simulator. With enough variability in the simulator, the real world may appear to the model as just another variation. We focus on the task of object localization, which is a stepping stone to general robotic manipulation skills. We find that it is possible to train a real-worl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.06907","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":""},"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":"1703.06907","created_at":"2026-05-18T00:48:13.968702+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.06907v1","created_at":"2026-05-18T00:48:13.968702+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.06907","created_at":"2026-05-18T00:48:13.968702+00:00"},{"alias_kind":"pith_short_12","alias_value":"QNXVDRPV6TVK","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_16","alias_value":"QNXVDRPV6TVKFIBM","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_8","alias_value":"QNXVDRPV","created_at":"2026-05-18T12:31:39.905425+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":13,"internal_anchor_count":4,"sample":[{"citing_arxiv_id":"1906.11633","citing_title":"ORRB -- OpenAI Remote Rendering Backend","ref_index":13,"is_internal_anchor":true},{"citing_arxiv_id":"2604.07303","citing_title":"Robots that learn to evaluate models of collective behavior","ref_index":17,"is_internal_anchor":true},{"citing_arxiv_id":"2605.19029","citing_title":"Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation","ref_index":46,"is_internal_anchor":true},{"citing_arxiv_id":"1910.07113","citing_title":"Solving Rubik's Cube with a Robot Hand","ref_index":106,"is_internal_anchor":true},{"citing_arxiv_id":"2604.02523","citing_title":"Tune to Learn: How Controller Gains Shape Robot Policy Learning","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11114","citing_title":"SEVO: Semantic-Enhanced Virtual Observation for Robust VLA Manipulation via Active Illumination and Data-Centric Collection","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2108.10470","citing_title":"Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08261","citing_title":"Computer Use at the Edge of the Statistical Precipice","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09699","citing_title":"A Real-Calibrated Synthetic-First Data Engine","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2604.11174","citing_title":"EmbodiedGovBench: A Benchmark for Governance, Recovery, and Upgrade Safety in Embodied Agent Systems","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12626","citing_title":"Habitat-GS: A High-Fidelity Navigation Simulator with Dynamic Gaussian Splatting","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07303","citing_title":"Robots that learn to evaluate models of collective behavior","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14262","citing_title":"GUI-Perturbed: Domain Randomization Reveals Systematic Brittleness in GUI Grounding Models","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QNXVDRPV6TVKFIBMZXHNRT4U6K","json":"https://pith.science/pith/QNXVDRPV6TVKFIBMZXHNRT4U6K.json","graph_json":"https://pith.science/api/pith-number/QNXVDRPV6TVKFIBMZXHNRT4U6K/graph.json","events_json":"https://pith.science/api/pith-number/QNXVDRPV6TVKFIBMZXHNRT4U6K/events.json","paper":"https://pith.science/paper/QNXVDRPV"},"agent_actions":{"view_html":"https://pith.science/pith/QNXVDRPV6TVKFIBMZXHNRT4U6K","download_json":"https://pith.science/pith/QNXVDRPV6TVKFIBMZXHNRT4U6K.json","view_paper":"https://pith.science/paper/QNXVDRPV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.06907&json=true","fetch_graph":"https://pith.science/api/pith-number/QNXVDRPV6TVKFIBMZXHNRT4U6K/graph.json","fetch_events":"https://pith.science/api/pith-number/QNXVDRPV6TVKFIBMZXHNRT4U6K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QNXVDRPV6TVKFIBMZXHNRT4U6K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QNXVDRPV6TVKFIBMZXHNRT4U6K/action/storage_attestation","attest_author":"https://pith.science/pith/QNXVDRPV6TVKFIBMZXHNRT4U6K/action/author_attestation","sign_citation":"https://pith.science/pith/QNXVDRPV6TVKFIBMZXHNRT4U6K/action/citation_signature","submit_replication":"https://pith.science/pith/QNXVDRPV6TVKFIBMZXHNRT4U6K/action/replication_record"}},"created_at":"2026-05-18T00:48:13.968702+00:00","updated_at":"2026-05-18T00:48:13.968702+00:00"}