{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007798081,0.0006087144,0.0008504267,0.0003906599,0.0003647681,0.0006417521,0.0005987344,0.0008356757,0.001942273],"category_scores_gemma":[0.005627073,0.0002531945,0.0002194824,0.0003327597,0.0005906409,0.001515459,0.0009577889,0.0008233513,0.0006470115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003804855,"about_ca_system_score_gemma":0.000410297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001016413,"about_ca_topic_score_gemma":0.0009515646,"domain_scores_codex":[0.999363,0.0001434955,0.00002946497,0.00009082673,0.0003290883,0.00004415874],"domain_scores_gemma":[0.9977906,0.001465433,0.0002303552,0.0002077825,0.0002611073,0.00004466464],"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":[0.0006165295,0.0001720338,0.001238024,0.0004213283,0.0000553057,0.0002039546,0.0002663128,0.5013949,0.06822713,0.008452175,0.0009897822,0.4179625],"study_design_scores_gemma":[0.00001615516,0.00009573345,0.0005491379,0.000009090519,0.000004892564,0.00009078351,0.00002338169,0.9810687,0.01469963,0.002678452,0.0007520883,0.00001191107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01564361,0.0001714856,0.9831545,0.00005789513,0.00001487473,0.00001856667,0.000007994508,0.0004714935,0.0004594857],"genre_scores_gemma":[0.7914892,0.0003138671,0.20489,0.00006627167,0.00003237805,0.00008909973,0.00006459244,0.0001374233,0.00291708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001942273,"threshold_uncertainty_score":0.006497562,"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."}}