{"id":"W4402354047","doi":"10.15607/rss.2024.xx.120","title":"DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset","year":2024,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Office of Naval Research; Toyota Research Institute; Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Ministry of Science and ICT, South Korea; National Science Foundation; Korea Advanced Institute of Science and Technology; National Research Foundation of Korea; Canadian Institute for Advanced Research; National Research Foundation; Engineering and Physical Sciences Research Council; UK Research and Innovation; Nvidia","keywords":"Scale (ratio); Robot; Computer science; Artificial intelligence; Computer vision; Geography; Cartography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002701879,0.002098361,0.0008822659,0.001405392,0.0008741894,0.0007342359,0.002384835,0.002213666,0.01816009],"category_scores_gemma":[0.001571331,0.0004758908,0.001290162,0.001369329,0.0004971853,0.0008604209,0.001303553,0.001498663,0.01679911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005757479,"about_ca_system_score_gemma":0.0009347875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02144493,"about_ca_topic_score_gemma":0.06928027,"domain_scores_codex":[0.9995394,0.00004539849,0.00002667878,0.0001758913,0.0001570161,0.00005559608],"domain_scores_gemma":[0.9994302,0.0001306273,0.00004172045,0.0002017085,0.0001266516,0.00006903769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000661524,0.0004493299,0.005632177,0.001727005,0.000218689,0.0004828067,0.0001095899,0.01346602,0.006316272,0.00131073,0.9077374,0.06188852],"study_design_scores_gemma":[0.0007080435,0.0006748638,0.04826733,0.0006430789,0.0002289173,0.001572827,0.0005335983,0.1036569,0.02064496,0.006399471,0.8163436,0.0003264956],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02694827,0.001476226,0.01164073,0.0004539309,0.0004129361,0.0003674045,0.925602,0.02202264,0.01107589],"genre_scores_gemma":[0.02439479,0.0002437685,0.01256211,0.0002175448,0.00002358443,0.000247426,0.9588102,0.0004752052,0.003025365],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02144493,"threshold_uncertainty_score":0.06075162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02100146027499242,"score_gpt":0.2588796327871848,"score_spread":0.2378781725121924,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}