{"id":"W2530993910","doi":"10.11159/cdsr16.137","title":"On the Use of Force Control with Compliant Sensing for Robot Safety","year":2016,"lang":"en","type":"article","venue":"Proceedings of the International Conference of Control, Dynamic systems, and Robotics","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Robot; Computer science; Control (management); Human–computer interaction; Artificial intelligence","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":[0.0006299555,0.0007437046,0.0004471051,0.0005222016,0.0003733248,0.00110366,0.0009237164,0.001312051,0.002025702],"category_scores_gemma":[0.001778795,0.0002133885,0.0004992956,0.0003337156,0.00195663,0.001180194,0.0008484615,0.0008996006,0.0003496394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004718378,"about_ca_system_score_gemma":0.0004143975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009615787,"about_ca_topic_score_gemma":0.0004995757,"domain_scores_codex":[0.9988679,0.0001542946,0.00003955739,0.0001522056,0.0007261725,0.00005999018],"domain_scores_gemma":[0.9987794,0.0007254595,0.0001872532,0.0001172979,0.0001693899,0.00002112022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003850453,0.0002273074,0.0009692333,0.001477467,0.00008088344,0.0008638567,0.0009467459,0.3228768,0.2343895,0.1696116,0.001954306,0.2662172],"study_design_scores_gemma":[0.00003885649,0.001260694,0.001720625,0.0003605354,0.00006009173,0.0006318443,0.0001063505,0.8599277,0.05951908,0.05696776,0.01926889,0.0001376916],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03215933,0.007362464,0.9339981,0.0006126835,0.0002028099,0.00009743026,0.0000281536,0.000456719,0.02508226],"genre_scores_gemma":[0.9059042,0.006108421,0.08109274,0.0002745535,0.0002714095,0.0001047735,0.0000441643,0.00006719074,0.006132539],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002025702,"threshold_uncertainty_score":0.006776631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02491598581494709,"score_gpt":0.2152906366740426,"score_spread":0.1903746508590955,"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."}}