{"id":"W1983852298","doi":"10.1109/ccece.2006.277568","title":"Predictive Teleoperation using Laser Rangefinder","year":2006,"lang":"en","type":"article","venue":"","topic":"Teleoperation and Haptic Systems","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Teleoperation; Control theory (sociology); Synchronization (alternating current); Controller (irrigation); Telerobotics; Master/slave; Model predictive control; Position (finance); Computer science; Stability (learning theory); Haptic technology; Control engineering; Collision avoidance; Collision; Simulation; Engineering; Robot; Mobile robot; Control (management); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004138673,0.00006778891,0.00006739322,0.00003162622,0.00004985932,0.00005653645,0.00002581748,0.00004384645,0.0003578227],"category_scores_gemma":[0.000002288114,0.00005979807,0.00001903662,0.00006612542,0.000007093382,0.0001221259,0.000003194729,0.00003607682,0.00007245919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003792383,"about_ca_system_score_gemma":0.000006821599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001900685,"about_ca_topic_score_gemma":0.0001514928,"domain_scores_codex":[0.9996181,0.000008687482,0.0001264402,0.00006976032,0.00008159543,0.00009544125],"domain_scores_gemma":[0.9998653,0.000007929567,0.000006229207,0.00007280408,0.00002773484,0.00002002179],"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":[0.00000528123,0.0000276142,0.002418081,0.00002465005,0.00003272873,0.000003878747,0.0002232848,0.9401753,0.0259006,0.00310024,0.02720541,0.0008829394],"study_design_scores_gemma":[0.000378653,0.00001046339,0.003655589,0.000008320161,0.000008825543,0.00001173027,0.0001084668,0.9738238,0.01446722,0.0000503806,0.007324358,0.0001521626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5111337,0.0001083256,0.2635809,0.00003079092,0.0005615542,0.0002250914,0.000005740358,0.0007429848,0.2236109],"genre_scores_gemma":[0.996228,0.000001421911,0.001207541,0.00003734905,0.000265096,0.000007511918,0.000008725169,0.00001429163,0.002230037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4850943,"threshold_uncertainty_score":0.3917907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00943004708348904,"score_gpt":0.192409157108537,"score_spread":0.1829791100250479,"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."}}