{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002827575,0.0007791337,0.0004332846,0.0003834972,0.0003518368,0.001061211,0.001409268,0.0008736199,0.02766681],"category_scores_gemma":[0.0009501925,0.0003173656,0.0004512827,0.0003788758,0.0003433226,0.001547849,0.001306052,0.001226697,0.01056496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005927695,"about_ca_system_score_gemma":0.0008608806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004284972,"about_ca_topic_score_gemma":0.008327879,"domain_scores_codex":[0.9998363,0.0000154513,0.000008023553,0.0000582405,0.00004296019,0.00003903156],"domain_scores_gemma":[0.9997982,0.00004007525,0.00001465471,0.00006777732,0.00005053864,0.00002873761],"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":[0.0006238788,0.0002486691,0.002081489,0.0005316166,0.0001035995,0.0002763861,0.0001058546,0.1000185,0.01547416,0.03484771,0.1707901,0.674898],"study_design_scores_gemma":[0.00008886082,0.0001305345,0.0006952004,0.00007092619,0.00002835543,0.0001607448,0.00004806815,0.8169897,0.01304476,0.03635762,0.1323525,0.00003280406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02417186,0.001801973,0.8340713,0.001305327,0.00105542,0.0002450747,0.007053333,0.06537371,0.06492204],"genre_scores_gemma":[0.3775908,0.001472264,0.4989471,0.002095111,0.000260685,0.0007060716,0.02351527,0.005754097,0.08965854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02766681,"threshold_uncertainty_score":0,"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."}}