{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JE7PP4FU6XROV5RQRLB2OVMLAD","short_pith_number":"pith:JE7PP4FU","schema_version":"1.0","canonical_sha256":"493ef7f0b4f5e2eaf6308ac3a7558b00cb4fc308ab28f3f106d287c217074ea8","source":{"kind":"arxiv","id":"2410.13126","version":1},"attestation_state":"computed","paper":{"title":"ALOHA Unleashed: A Simple Recipe for Robot Dexterity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Ayzaan Wahid, Chelsea Finn, Danny Driess, Jonathan Tompson, Kamyar Ghasemipour, Pete Florence, Tony Z. Zhao","submitted_at":"2024-10-17T01:29:49Z","abstract_excerpt":"Recent work has shown promising results for learning end-to-end robot policies using imitation learning. In this work we address the question of how far can we push imitation learning for challenging dexterous manipulation tasks. We show that a simple recipe of large scale data collection on the ALOHA 2 platform, combined with expressive models such as Diffusion Policies, can be effective in learning challenging bimanual manipulation tasks involving deformable objects and complex contact rich dynamics. We demonstrate our recipe on 5 challenging real-world and 3 simulated tasks and demonstrate "},"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":"2410.13126","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-10-17T01:29:49Z","cross_cats_sorted":[],"title_canon_sha256":"57ef512f795e5bc4a319b0cf0179a74098ee2686375efd8ee1f8338d1bfd9f8c","abstract_canon_sha256":"ee282a79b8b994a4b15da5735289a8f1610ec1a107773c7ca8202cd6fa49b275"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:21:56.030241Z","signature_b64":"AdbKxgJGLBPnJTL24CHlHcAj8v3viJsQeoXfqMvAY76ySpu91gkrh/qvFeRCB8QUepgncLp+euz7PAwrCRirAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"493ef7f0b4f5e2eaf6308ac3a7558b00cb4fc308ab28f3f106d287c217074ea8","last_reissued_at":"2026-07-05T09:21:56.029730Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:21:56.029730Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ALOHA Unleashed: A Simple Recipe for Robot Dexterity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Ayzaan Wahid, Chelsea Finn, Danny Driess, Jonathan Tompson, Kamyar Ghasemipour, Pete Florence, Tony Z. Zhao","submitted_at":"2024-10-17T01:29:49Z","abstract_excerpt":"Recent work has shown promising results for learning end-to-end robot policies using imitation learning. In this work we address the question of how far can we push imitation learning for challenging dexterous manipulation tasks. We show that a simple recipe of large scale data collection on the ALOHA 2 platform, combined with expressive models such as Diffusion Policies, can be effective in learning challenging bimanual manipulation tasks involving deformable objects and complex contact rich dynamics. We demonstrate our recipe on 5 challenging real-world and 3 simulated tasks and demonstrate "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.13126","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/2410.13126/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":"2410.13126","created_at":"2026-07-05T09:21:56.029792+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.13126v1","created_at":"2026-07-05T09:21:56.029792+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.13126","created_at":"2026-07-05T09:21:56.029792+00:00"},{"alias_kind":"pith_short_12","alias_value":"JE7PP4FU6XRO","created_at":"2026-07-05T09:21:56.029792+00:00"},{"alias_kind":"pith_short_16","alias_value":"JE7PP4FU6XROV5RQ","created_at":"2026-07-05T09:21:56.029792+00:00"},{"alias_kind":"pith_short_8","alias_value":"JE7PP4FU","created_at":"2026-07-05T09:21:56.029792+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":19,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25575","citing_title":"One Body, Two Minds: Variable Autonomy Approach for a Co-embodied Robotic Hand","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27375","citing_title":"Scalable Behavior Cloning with Open Data, Training, and Evaluation","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2606.20285","citing_title":"Co-VLA: Coordination-Aware Structured Action Modeling for Dual-Arm Vision-Language-Action Systems","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06556","citing_title":"Robots Need More than VLA and World Models","ref_index":114,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06491","citing_title":"TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2606.32009","citing_title":"Human-as-Humanoid: Enabling Zero-Shot Humanoid Learning from Ego-Exo Human Videos with Human-Aligned Embodiments","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2606.29941","citing_title":"Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation","ref_index":71,"is_internal_anchor":false},{"citing_arxiv_id":"2506.15953","citing_title":"ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2510.08547","citing_title":"R2RGEN: Real-to-Real 3D Data Generation for Spatially Generalized Manipulation","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2506.15799","citing_title":"Steering Your Diffusion Policy with Latent Space Reinforcement Learning","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2512.07371","citing_title":"ESPADA: Execution Speedup via Semantics Aware Demonstration Data Downsampling for Imitation Learning","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2602.00937","citing_title":"CLAMP: Contrastive Learning for 3D Multi-View Action-Conditioned Robotic Manipulation Pretraining","ref_index":67,"is_internal_anchor":false},{"citing_arxiv_id":"2506.07339","citing_title":"Real-Time Execution of Action Chunking Flow Policies","ref_index":69,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10903","citing_title":"CapVector: Learning Transferable Capability Vectors in Parametric Space for Vision-Language-Action Models","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2501.09747","citing_title":"FAST: Efficient Action Tokenization for Vision-Language-Action Models","ref_index":71,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02881","citing_title":"MolmoAct2: Action Reasoning Models for Real-world Deployment","ref_index":49,"is_internal_anchor":false},{"citing_arxiv_id":"2502.19645","citing_title":"Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2410.24164","citing_title":"$\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17887","citing_title":"StableIDM: Stabilizing Inverse Dynamics Model against Manipulator Truncation via Spatio-Temporal Refinement","ref_index":54,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JE7PP4FU6XROV5RQRLB2OVMLAD","json":"https://pith.science/pith/JE7PP4FU6XROV5RQRLB2OVMLAD.json","graph_json":"https://pith.science/api/pith-number/JE7PP4FU6XROV5RQRLB2OVMLAD/graph.json","events_json":"https://pith.science/api/pith-number/JE7PP4FU6XROV5RQRLB2OVMLAD/events.json","paper":"https://pith.science/paper/JE7PP4FU"},"agent_actions":{"view_html":"https://pith.science/pith/JE7PP4FU6XROV5RQRLB2OVMLAD","download_json":"https://pith.science/pith/JE7PP4FU6XROV5RQRLB2OVMLAD.json","view_paper":"https://pith.science/paper/JE7PP4FU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.13126&json=true","fetch_graph":"https://pith.science/api/pith-number/JE7PP4FU6XROV5RQRLB2OVMLAD/graph.json","fetch_events":"https://pith.science/api/pith-number/JE7PP4FU6XROV5RQRLB2OVMLAD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JE7PP4FU6XROV5RQRLB2OVMLAD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JE7PP4FU6XROV5RQRLB2OVMLAD/action/storage_attestation","attest_author":"https://pith.science/pith/JE7PP4FU6XROV5RQRLB2OVMLAD/action/author_attestation","sign_citation":"https://pith.science/pith/JE7PP4FU6XROV5RQRLB2OVMLAD/action/citation_signature","submit_replication":"https://pith.science/pith/JE7PP4FU6XROV5RQRLB2OVMLAD/action/replication_record"}},"created_at":"2026-07-05T09:21:56.029792+00:00","updated_at":"2026-07-05T09:21:56.029792+00:00"}