{"id":"W2126494634","doi":"10.1109/iros.2008.4650572","title":"Online Contact Impedance Identification for Robotic Systems","year":2008,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Identification (biology); Computer science; Sensitivity (control systems); Noise (video); Convergence (economics); Electrical impedance; Computational complexity theory; Rate of convergence; Impedance control; Algorithm; Robot; Artificial intelligence; Key (lock); Electronic 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004668838,0.00005644648,0.00008236038,0.00003591353,0.00005623282,0.00001789861,0.00004531345,0.00002910312,0.00002214552],"category_scores_gemma":[0.00002077957,0.00005546864,0.00002792928,0.0000568423,0.000004044416,0.00009372693,0.000002547014,0.00004318545,0.00005532214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002764651,"about_ca_system_score_gemma":0.000004607481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001222721,"about_ca_topic_score_gemma":0.000005452172,"domain_scores_codex":[0.9996061,0.000006830868,0.0001606957,0.00007441961,0.00005653983,0.00009543139],"domain_scores_gemma":[0.9997894,0.00003358653,0.00002086579,0.00009711176,0.00003209521,0.00002694923],"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":[0.000001094254,0.000006836181,0.0004493782,0.00003706337,0.000007888225,4.86068e-7,0.00006724437,0.9926118,0.003667013,0.001569952,0.001384086,0.0001971356],"study_design_scores_gemma":[0.0001488225,0.000007860577,0.02218815,0.000008221062,0.000003866462,0.00000877101,0.00005170607,0.975503,0.0001943601,0.000005595602,0.001805761,0.00007391182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07169504,0.0002481748,0.9258463,0.00003248662,0.0005431388,0.0002194701,4.671278e-7,0.0003920202,0.001022883],"genre_scores_gemma":[0.9958689,0.00002529086,0.0007430919,0.00001507193,0.0001097088,0.00001758716,0.00003680175,0.00001717038,0.003166393],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9251032,"threshold_uncertainty_score":0.2261945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04726386404454511,"score_gpt":0.2588289096060373,"score_spread":0.2115650455614922,"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."}}