{"id":"W2068769351","doi":"10.2316/journal.206.2010.4.206-3434","title":"BALANCING CONTROL OF LEG EXOSKELETON USING ZMP-BASED JACOBIAN COMPENSATION","year":2010,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Advanced Computing and Algorithms","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Exoskeleton; Jacobian matrix and determinant; Compensation (psychology); Control theory (sociology); Computer science; Control (management); Control engineering; Engineering; Mathematics; Simulation; Artificial intelligence; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002632925,0.0004671103,0.0003276009,0.0002730614,0.0004371006,0.0004545708,0.0003599157,0.00029809,0.001914922],"category_scores_gemma":[0.0004301752,0.000180962,0.0001758606,0.0001817576,0.0001860538,0.0002557881,0.0003639672,0.0001780209,0.0003297378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001181864,"about_ca_system_score_gemma":0.0002579508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00180899,"about_ca_topic_score_gemma":0.002833517,"domain_scores_codex":[0.9999083,0.00001473615,0.000008920544,0.00002022074,0.0000349564,0.00001287042],"domain_scores_gemma":[0.9998778,0.00002177651,0.00003542925,0.00001194509,0.00004355384,0.000009531199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001719537,0.0003524033,0.002182685,0.0008639643,0.0001327845,0.0005796367,0.0005428733,0.1812771,0.4279637,0.004865668,0.003261562,0.3762581],"study_design_scores_gemma":[0.0003339638,0.001333034,0.01207707,0.00007090159,0.00009970189,0.0002964466,0.0001145046,0.9482382,0.03037577,0.002051661,0.004967235,0.00004160654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3052445,0.0007328113,0.6761026,0.0003305417,0.0003172559,0.0002323149,0.0001235701,0.001343098,0.01557327],"genre_scores_gemma":[0.9890383,0.00007698884,0.008976557,0.00002055184,0.00001834677,0.00004722999,0.00003024305,0.0000129397,0.001778717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001914922,"threshold_uncertainty_score":0.006406069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01338925639018504,"score_gpt":0.314598555410007,"score_spread":0.301209299019822,"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."}}