{"id":"W2739330054","doi":"10.1145/3072959.3073602","title":"DeepLoco","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":512,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Terrain; Reinforcement learning; Exploit; Robustness (evolution); Artificial intelligence; Variety (cybernetics); Controller (irrigation); Control engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005014885,0.00009549971,0.00008805624,0.00008060963,0.0003792795,0.00007897383,0.0003668871,0.00006895186,0.0001356651],"category_scores_gemma":[0.0000134434,0.00009543216,0.00009193811,0.00005078546,0.00004982442,0.000130259,0.00000160985,0.0002009019,0.0001234151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001250421,"about_ca_system_score_gemma":0.000005577239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008620373,"about_ca_topic_score_gemma":0.00005457298,"domain_scores_codex":[0.9995551,0.000007385547,0.00009733079,0.00009633314,0.0001011048,0.0001427702],"domain_scores_gemma":[0.9989848,0.00002906453,0.00001624822,0.000882054,0.0000206182,0.00006719881],"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":[0.00005596978,0.0004798437,0.00147047,0.0001245183,0.0007620186,0.0000464993,0.0006389499,0.2146089,0.002484254,0.02419578,0.003896382,0.7512364],"study_design_scores_gemma":[0.01321727,0.0006726015,0.1343952,0.0003471639,0.0007351278,0.0001188544,0.0004066616,0.5152656,0.03146644,0.1113289,0.1881461,0.003900126],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009903504,0.00005424384,0.9781867,0.002084648,0.001085098,0.000168962,0.000009621142,0.0005860578,0.007921171],"genre_scores_gemma":[0.9983249,0.000169805,0.001006261,0.0002013749,0.00003299511,0.00002714192,8.353397e-7,0.00002177167,0.0002149231],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9884214,"threshold_uncertainty_score":0.3891611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01949852847396992,"score_gpt":0.2378628009627653,"score_spread":0.2183642724887953,"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."}}