{"id":"W4317792690","doi":"10.1109/wsc57314.2022.10015308","title":"Accelerating Training of Reinforcement Learning-Based Construction Robots in Simulation Using Demonstrations Collected in Virtual Reality","year":2022,"lang":"en","type":"article","venue":"2022 Winter Simulation Conference (WSC)","topic":"BIM and Construction Integration","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Reinforcement learning; Robot; Computer science; Flexibility (engineering); Task (project management); Economic shortage; Human–computer interaction; Virtual reality; Artificial intelligence; Simulation; Engineering; Systems engineering","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":[0.001270821,0.0007954509,0.000463662,0.0002604334,0.0002254353,0.0003856533,0.000996244,0.000692133,0.001837753],"category_scores_gemma":[0.005163634,0.0003942746,0.0003000454,0.0001212947,0.0005261475,0.0005924205,0.0008644686,0.0008990227,0.0003051947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004484816,"about_ca_system_score_gemma":0.0008062815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004526115,"about_ca_topic_score_gemma":0.004846355,"domain_scores_codex":[0.9995348,0.0001913652,0.00002725138,0.00009001643,0.00007428719,0.00008232383],"domain_scores_gemma":[0.9973742,0.001631488,0.0002186849,0.0003122436,0.0002736429,0.0001897295],"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":[0.0002381044,0.0004573968,0.002695691,0.00008698146,0.00003971777,0.00008399956,0.0001449871,0.9566212,0.007501225,0.0006432331,0.0005095946,0.0309779],"study_design_scores_gemma":[0.00003699482,0.0002175737,0.0008162486,0.0000106955,0.000008386691,0.00001491515,0.0000257394,0.9940035,0.003938025,0.0003661008,0.0005514738,0.00001030779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7105865,0.0002018096,0.2805152,0.0003078608,0.0001044529,0.0002384394,0.0001156263,0.002531691,0.005398402],"genre_scores_gemma":[0.9603689,0.00005128398,0.03851103,0.00004737997,0.000005573753,0.0001079435,0.0001187213,0.0000343396,0.0007547512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004526115,"threshold_uncertainty_score":0.008999527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07354045546339771,"score_gpt":0.2889406442721747,"score_spread":0.215400188808777,"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."}}