{"id":"W4402475277","doi":"10.1109/tmech.2024.3451228","title":"Online Evaluation for Learning Feasible Robotic Grasps With Physical Constraints","year":2024,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Teaching and Learning Programming","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Computer science; Online learning; Human–computer interaction; Artificial intelligence; Multimedia","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":[0.0007565122,0.0002302624,0.0002105983,0.0002217188,0.0004486701,0.0003657568,0.0003313625,0.00008600571,0.00001625172],"category_scores_gemma":[0.00002338027,0.0002014,0.0001761997,0.0004852705,0.00007118932,0.0003929492,0.000003697298,0.0009046862,0.00004747447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002086421,"about_ca_system_score_gemma":0.0003559389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001375618,"about_ca_topic_score_gemma":0.00002442895,"domain_scores_codex":[0.9980873,0.0001887369,0.0001914554,0.0005921179,0.0005063734,0.0004339627],"domain_scores_gemma":[0.999067,0.0003231886,0.00005744397,0.0003324571,0.0001131433,0.0001067576],"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":[0.00001196413,0.0001945062,0.00000162173,0.00004147699,0.00006451899,0.000002690276,0.0008341789,0.4452253,0.0001733393,0.00734398,0.00001313994,0.5460933],"study_design_scores_gemma":[0.0006019368,0.0008766412,0.00001033288,0.0001821984,0.0001481975,0.00003657097,0.0002386988,0.9895865,0.0006100907,0.001068475,0.006362861,0.0002775207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01183532,0.000159927,0.9847763,0.001061455,0.0007205378,0.0004812231,0.000006857894,0.0009190865,0.00003930628],"genre_scores_gemma":[0.885474,0.00001030238,0.1138232,0.00003541232,0.0001056645,0.0001119698,0.00001741759,0.00003777063,0.0003842202],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8736387,"threshold_uncertainty_score":0.8212855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0343441784674651,"score_gpt":0.3141835014086969,"score_spread":0.2798393229412318,"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."}}