{"id":"W4294690836","doi":"10.23919/acc53348.2022.9867729","title":"Structured Online Learning for Low-Level Control of Quadrotors","year":2022,"lang":"en","type":"article","venue":"2022 American Control Conference (ACC)","topic":"Adaptive Dynamic Programming Control","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Controller (irrigation); Reinforcement learning; Artificial neural network; Identifier; Set (abstract data type); Control engineering; Parameterized complexity; Control theory (sociology); Artificial intelligence; Control (management); Engineering; Algorithm","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008658804,0.0004623519,0.001180687,0.0003391065,0.00052082,0.0001290211,0.002444379,0.00005941977,0.0001662594],"category_scores_gemma":[0.000656265,0.0004822164,0.0004008086,0.0008611748,0.0004205617,0.000316799,0.000458703,0.0007921812,0.000004560715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002526435,"about_ca_system_score_gemma":0.0006643453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004883318,"about_ca_topic_score_gemma":0.000112247,"domain_scores_codex":[0.9956869,0.0007108411,0.000832167,0.0009893809,0.0008685294,0.0009121827],"domain_scores_gemma":[0.995999,0.001060472,0.001213457,0.0009272009,0.0005681232,0.0002317592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001411014,0.0006383317,0.009268006,0.00006579001,0.0008466226,0.00004396647,0.001636936,0.02079654,0.03369725,0.07823071,0.0002968399,0.853068],"study_design_scores_gemma":[0.00744717,0.002211236,0.00730489,0.00001661876,0.0001051006,0.00001875787,0.001440913,0.9706376,0.0001129083,0.001732273,0.008281588,0.0006909463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0418132,0.0001457594,0.953084,0.001589576,0.0004949711,0.001852247,0.0006456894,0.0002849602,0.00008960701],"genre_scores_gemma":[0.9851468,0.000005548908,0.01223015,0.001059234,0.0001090164,0.0009721842,0.00006970654,0.00005014592,0.0003572272],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9498411,"threshold_uncertainty_score":0.999763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01740815177442727,"score_gpt":0.2586946933443374,"score_spread":0.2412865415699101,"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."}}