{"paper":{"title":"Robotic Control via Embodied Chain-of-Thought Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Embodied chain-of-thought reasoning trains VLAs to output grounded plans and visuals before actions, raising OpenVLA success by 28 percent.","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Chelsea Finn, Karl Pertsch, Micha{\\l} Zawalski, Oier Mees, Sergey Levine, William Chen","submitted_at":"2024-07-11T17:31:01Z","abstract_excerpt":"A key limitation of learned robot control policies is their inability to generalize outside their training data. Recent works on vision-language-action models (VLAs) have shown that the use of large, internet pre-trained vision-language models as the backbone of learned robot policies can substantially improve their robustness and generalization ability. Yet, one of the most exciting capabilities of large vision-language models in other domains is their ability to reason iteratively through complex problems. Can that same capability be brought into robotics to allow policies to improve perform"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"ECoT increases the absolute success rate of OpenVLA, the current strongest open-source VLA policy, by 28% across challenging generalization tasks, without any additional robot training data.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The synthetic data generation pipeline produces reasoning traces that are both accurate enough to supervise the model and sufficiently diverse to improve generalization rather than overfitting to the generation heuristics.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Training VLAs to perform embodied chain-of-thought reasoning about plans, sub-tasks, motions, and grounded visual features before acting raises OpenVLA success rates by 28% on challenging generalization tasks without new robot data.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Embodied chain-of-thought reasoning trains VLAs to output grounded plans and visuals before actions, raising OpenVLA success by 28 percent.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"1e95f37dfcfa3fd3139ba5bfdf7503823af107ebeabbb176dd60e81bf771352a"},"source":{"id":"2407.08693","kind":"arxiv","version":3},"verdict":{"id":"ba2308ba-b3f6-4bbe-95e5-0f44d7a7f874","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T08:08:48.921925Z","strongest_claim":"ECoT increases the absolute success rate of OpenVLA, the current strongest open-source VLA policy, by 28% across challenging generalization tasks, without any additional robot training data.","one_line_summary":"Training VLAs to perform embodied chain-of-thought reasoning about plans, sub-tasks, motions, and grounded visual features before acting raises OpenVLA success rates by 28% on challenging generalization tasks without new robot data.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The synthetic data generation pipeline produces reasoning traces that are both accurate enough to supervise the model and sufficiently diverse to improve generalization rather than overfitting to the generation heuristics.","pith_extraction_headline":"Embodied chain-of-thought reasoning trains VLAs to output grounded plans and visuals before actions, raising OpenVLA success by 28 percent."},"references":{"count":118,"sample":[{"doi":"","year":2022,"title":"A. Agarwal, A. Kumar, J. Malik, and D. Pathak. Legged locomotion in challenging terrains using egocentric vision, 2022","work_id":"b341c505-ba1d-4447-ae59-f044b8548c55","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"T. Z. Zhao, V . Kumar, S. Levine, and C. Finn. Learning fine-grained bimanual manipulation with low-cost hardware, 2023","work_id":"940e6de2-97a0-4454-806a-94c0994d1186","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation","work_id":"5f6ff8ef-ed80-4c00-92c2-361c80bf8448","ref_index":3,"cited_arxiv_id":"2401.02117","is_internal_anchor":true},{"doi":"","year":2022,"title":"R. Bommasani, D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill, E. Brynjolfsson, S. Buch, D. Card, R. Castellon, N. Chatterji, A. Chen, K. C","work_id":"580d566d-f8f9-4c12-b7f9-35cfad2dbaad","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"A. Brohan, N. Brown, J. Carbajal, Y . Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn, P. Florence, C. Fu, M. G. Arenas, K. Gopalakrishnan, K. Han, K. Hausman, A. Herzog, J. H","work_id":"4720ca2c-3947-43dd-8276-6801cea0a7ef","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":118,"snapshot_sha256":"0505ae30557932879eb321cad49d6ad1e5108b48d93b96328f83b00a6224ad27","internal_anchors":2},"formal_canon":{"evidence_count":2,"snapshot_sha256":"41b360fcccc4df08dd6d710018e583705ec1716c285131c5f02ea538855243a6"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}