{"id":"W2095928699","doi":"10.1109/wcica.2006.1713297","title":"A Genetic Algorithms Approach to Model Parameter Estimation of a Robot Joint with Torque Sensing","year":2006,"lang":"en","type":"article","venue":"","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Torque; Genetic algorithm; Joint (building); Computer science; Identification (biology); Estimation theory; Robot; Control theory (sociology); Algorithm; Artificial intelligence; Engineering; Machine learning; Control (management); Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005417195,0.00102215,0.0007980297,0.0008642573,0.0004872841,0.0007158615,0.001347069,0.001289755,0.001239722],"category_scores_gemma":[0.001732352,0.00043857,0.0007727518,0.000768695,0.0008149294,0.0005933591,0.0005494176,0.001299682,0.0004520019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006114399,"about_ca_system_score_gemma":0.001084661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009762284,"about_ca_topic_score_gemma":0.005248037,"domain_scores_codex":[0.9996654,0.0001090579,0.00001537625,0.00005238618,0.0001311621,0.00002658856],"domain_scores_gemma":[0.9996326,0.0001910169,0.00004976779,0.00003221833,0.00008302987,0.00001126838],"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":[0.00001717863,0.00003107404,0.0001785755,0.00004424386,0.00005194323,0.00006169952,0.0000639268,0.9227461,0.003473771,0.01302108,0.0004298515,0.05988063],"study_design_scores_gemma":[0.000006020855,0.00002488502,0.00005939373,0.000006203292,0.000009747866,0.00002711345,0.00000496164,0.9940531,0.0007573047,0.004340923,0.000701567,0.000008900555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001627663,0.00009899198,0.9972256,0.00004434349,0.00001355295,0.00001553883,0.00000593298,0.0001734855,0.0007947879],"genre_scores_gemma":[0.2487026,0.0006181065,0.7459732,0.0001167935,0.00007295116,0.0004044144,0.0001046008,0.0001064696,0.003900872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009762284,"threshold_uncertainty_score":0.01941091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01160357247159502,"score_gpt":0.1945916803590207,"score_spread":0.1829881078874256,"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."}}