{"id":"W3048046124","doi":"10.1109/tie.2020.3013798","title":"Learning-Based Terrain Identification With Proprioceptive Sensors for Mobile Robots","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for Central Universities of the Central South University; National Natural Science Foundation of China","keywords":"Terrain; Computer science; Artificial intelligence; Torque; Mobile robot; Gaussian process; Identification (biology); SIGNAL (programming language); Robot; Hyperparameter; Machine learning; Computer vision; Gaussian; Control theory (sociology); Simulation","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":[0.0001535497,0.0002078946,0.0001972061,0.00009380168,0.0003547594,0.0002036469,0.0004376358,0.000170083,0.00001944919],"category_scores_gemma":[0.00001623287,0.000183347,0.00009168374,0.0007083193,0.00005076291,0.0003082269,0.000001439906,0.0006572609,0.00002464263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001255136,"about_ca_system_score_gemma":0.0006160449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000104201,"about_ca_topic_score_gemma":0.00001884906,"domain_scores_codex":[0.998443,0.00007156073,0.0002724622,0.0005370441,0.0002752737,0.0004006593],"domain_scores_gemma":[0.9991976,0.00009871129,0.0001647659,0.0002451486,0.0001523827,0.0001413924],"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":[0.0005621197,0.0002645132,0.000007047706,0.00003871393,0.00008461977,0.000004176606,0.001253647,0.8856207,0.003041385,0.001264197,0.0003156816,0.1075432],"study_design_scores_gemma":[0.003596277,0.009632627,0.000007478281,0.00006511533,0.00008791958,0.00001086508,0.000166061,0.6861312,0.2907534,0.0005205414,0.008358079,0.0006703675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007866116,0.00001294058,0.9887027,0.00209598,0.0001480018,0.0008716463,0.00001180356,0.0002502613,0.00004058225],"genre_scores_gemma":[0.9953712,0.000005298343,0.003795237,0.0001910997,0.00009309611,0.0003420639,0.00000574749,0.00002059393,0.000175706],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.987505,"threshold_uncertainty_score":0.7476673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02419166421159674,"score_gpt":0.2426748559565158,"score_spread":0.2184831917449191,"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."}}