{"id":"W4389540788","doi":"10.17118/11143/21092","title":"Reinforcement learning based dynamic path following of an industrialrobot","year":2023,"lang":"en","type":"article","venue":"","topic":"Elevator Systems and Control","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Reinforcement learning; Computer science; Path (computing); Reinforcement; Robot; Robot learning; Mobile robot; Artificial intelligence; Engineering; Structural engineering; Computer network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002426758,0.00008108138,0.0001513624,0.00008401994,0.00003275631,0.00001323935,0.00007239676,0.00006073846,0.00004678925],"category_scores_gemma":[0.0000194069,0.0000756269,0.0000666276,0.0002170187,0.000003653316,0.00007005413,0.000009285823,0.0001010093,0.00002973013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003006867,"about_ca_system_score_gemma":0.00001483457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005609413,"about_ca_topic_score_gemma":0.000009604982,"domain_scores_codex":[0.9993736,0.00001929883,0.0002145778,0.00008034546,0.0001421058,0.0001700518],"domain_scores_gemma":[0.9997529,0.00003136777,0.00002335184,0.0001360625,0.0000110559,0.00004522808],"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":[0.000003074851,0.000002836367,0.0005642393,0.00003230531,0.00003478075,0.000006956005,0.00006871832,0.9763639,0.01808367,0.0001085686,0.00005554393,0.004675386],"study_design_scores_gemma":[0.0006157969,0.00005869074,0.0004612562,0.00004027627,0.000009404569,1.785272e-7,0.0001949267,0.9960584,0.001794505,0.000004436845,0.0006676892,0.00009441921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9696261,0.00003914026,0.0251243,0.00001886936,0.0004537249,0.000185197,8.00364e-7,0.0007735996,0.003778282],"genre_scores_gemma":[0.9989804,0.000001700546,0.00008744519,0.000008116986,0.00003065439,0.00001704751,0.00001559491,0.00002160727,0.0008374041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02935434,"threshold_uncertainty_score":0.3083976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01026569094237768,"score_gpt":0.2211338024014164,"score_spread":0.2108681114590387,"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."}}