{"id":"W2033205747","doi":"10.4028/www.scientific.net/aef.2-3.414","title":"Bipedal Walking Trajectory Generation Using Tchebychev Method","year":2011,"lang":"en","type":"article","venue":"Advanced engineering forum","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Torque; Trajectory; Set (abstract data type); Control theory (sociology); Robot; Computer science; Quadratic programming; Gait; Joint (building); Biped robot; Ankle; Mathematical optimization; Mathematics; Engineering; Artificial intelligence; Physical medicine and rehabilitation; Control (management)","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":[0.0001079951,0.0002128186,0.0001924841,0.0001278863,0.00005240747,0.00001752793,0.0001161565,0.00008927862,0.00006669386],"category_scores_gemma":[0.00001923623,0.0002357856,0.00008327259,0.0001708647,0.00000749196,0.0002837734,0.00001595782,0.0001768693,0.00001706659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001061034,"about_ca_system_score_gemma":0.000009600423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006474583,"about_ca_topic_score_gemma":0.000004499579,"domain_scores_codex":[0.9990531,0.00001262688,0.0002381985,0.0001844252,0.0001116063,0.0004000904],"domain_scores_gemma":[0.9996158,0.00002069972,0.0000243779,0.0002177037,0.00002539607,0.00009595665],"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":[0.000002187611,0.000007912656,0.00002579927,0.0000196711,0.00003006955,0.000003839731,0.0002276557,0.7518422,0.2265109,0.0007484328,0.00002033816,0.02056097],"study_design_scores_gemma":[0.0003824178,0.00001891222,0.0002447775,0.00002150771,0.00001895634,0.00001695997,0.00006377809,0.9421943,0.05571391,0.00006058446,0.0009818118,0.0002821304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04815836,0.0003930302,0.9484462,0.000009732361,0.001140615,0.0001430374,0.000001890701,0.0007608841,0.0009462662],"genre_scores_gemma":[0.735229,0.00001709507,0.2644691,0.00003418024,0.0001245818,0.00002216807,0.000004442585,0.00006421113,0.00003521235],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6870706,"threshold_uncertainty_score":0.9615057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02472503335598018,"score_gpt":0.2317014452832104,"score_spread":0.2069764119272302,"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."}}