{"id":"W7126940406","doi":"","title":"∇<i>Sim</i>:DIFFERENTIABLE SIMULATION FOR SYSTEM IDENTIFICATION AND VISUOMOTOR CONTROL","year":2021,"lang":"en","type":"article","venue":"Research at the University of Copenhagen (University of Copenhagen)","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; McGill University; Université de Montréal; Vector Institute; Mila - Quebec Artificial Intelligence Institute","funders":"Institut de Valorisation des Données; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Rendering (computer graphics); Differentiable function; Pixel; Computation; Physical system; Backpropagation; Process (computing); System identification; Control system","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003382084,0.0006723477,0.0004562196,0.0002834085,0.0002744814,0.0006510532,0.0008982915,0.000771502,0.003561908],"category_scores_gemma":[0.001385721,0.0003047826,0.0004456589,0.0002653588,0.0008972231,0.0007010782,0.0009530971,0.001305143,0.0007701695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006385293,"about_ca_system_score_gemma":0.0009507961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00629228,"about_ca_topic_score_gemma":0.006394416,"domain_scores_codex":[0.9998704,0.00003097273,0.000005193542,0.00003153014,0.00004831429,0.0000135444],"domain_scores_gemma":[0.9996701,0.0001533049,0.00004206551,0.00006331255,0.00004252837,0.00002859566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004945691,0.00002796491,0.0004615326,0.00005478633,0.00001879084,0.000070447,0.00004716269,0.9269301,0.005986017,0.02360907,0.00155935,0.04118528],"study_design_scores_gemma":[0.0000014452,0.000004552855,0.00001927125,0.00000153801,6.104086e-7,0.000003699183,0.000001396959,0.996339,0.0006182008,0.002562226,0.0004469443,0.000001222957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005141343,0.00005791108,0.9914373,0.00014017,0.00002160756,0.00001288402,0.00004719732,0.001335248,0.001806419],"genre_scores_gemma":[0.5317721,0.0002381199,0.4617903,0.0001723549,0.00004482425,0.0001442024,0.0002768299,0.0006979129,0.004863456],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00629228,"threshold_uncertainty_score":0.01251131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03414062862038312,"score_gpt":0.2559113466959937,"score_spread":0.2217707180756106,"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."}}