{"id":"W2938668785","doi":"10.3390/robotics8020033","title":"Impedance Control Self-Calibration of a Collaborative Robot Using Kinematic Coupling","year":2019,"lang":"en","type":"article","venue":"Robotics","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Laser tracker; Calibration; Kinematics; Robot; Impedance control; Electrical impedance; Coupling (piping); Robot calibration; Laser; Fiducial marker; Computer science; Engineering; Simulation; Control theory (sociology); Robot kinematics; Artificial intelligence; Control (management); Mobile robot; Mechanical engineering; Electrical engineering; Optics; Mathematics; Physics","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":[0.0008696761,0.0006279857,0.0005437324,0.0005464337,0.0004347283,0.0008593119,0.001274357,0.0006942545,0.001307909],"category_scores_gemma":[0.001929823,0.0003253859,0.0003581309,0.0003661563,0.0008727617,0.001078418,0.002054162,0.000519949,0.0006140012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002957963,"about_ca_system_score_gemma":0.0004527196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00057273,"about_ca_topic_score_gemma":0.000460645,"domain_scores_codex":[0.998684,0.0001760031,0.00006944391,0.000358103,0.0006156994,0.00009675772],"domain_scores_gemma":[0.9989445,0.0002367,0.0002616936,0.0002816812,0.0002185452,0.00005694299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003995513,0.0003225755,0.002413156,0.0002950327,0.00008023595,0.0004800174,0.001142767,0.1358568,0.459024,0.009546706,0.00105868,0.3893805],"study_design_scores_gemma":[0.0001239175,0.001446863,0.004300739,0.00004605945,0.00006650448,0.0008743487,0.0001537281,0.7748914,0.2009427,0.004490658,0.01254707,0.0001161815],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04202008,0.00005904348,0.9546432,0.0000365511,0.00002717265,0.00005342972,0.000006140062,0.001026632,0.002127688],"genre_scores_gemma":[0.8790177,0.00004948903,0.1183239,0.00004364231,0.00001964978,0.00009788731,0.00002292945,0.00006904227,0.002355669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001307909,"threshold_uncertainty_score":0.004599333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01080300189091654,"score_gpt":0.2274064773329384,"score_spread":0.2166034754420219,"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."}}