{"id":"W2120227055","doi":"10.1109/isic.1992.225124","title":"Adaptive neural networks for vision-guided position control of a robot arm","year":2003,"lang":"en","type":"article","venue":"","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Controller (irrigation); Computer science; Inverse kinematics; Artificial neural network; Artificial intelligence; Robot; Robotic arm; Computer vision; Kinematics; Position (finance); Control theory (sociology); Projection (relational algebra); Perspective (graphical); Control engineering; Control (management); Engineering; Algorithm","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":[0.0001931787,0.0003794097,0.0001908141,0.0001907204,0.0001541691,0.0003670163,0.0005687382,0.0006220422,0.001332285],"category_scores_gemma":[0.000737097,0.000169856,0.0001539144,0.0002473682,0.0003061923,0.0003387848,0.0002537709,0.0005584068,0.0002731372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005368991,"about_ca_system_score_gemma":0.0003330186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007222711,"about_ca_topic_score_gemma":0.007111002,"domain_scores_codex":[0.9998945,0.0000193426,0.000006281857,0.00002225683,0.00004340633,0.00001419313],"domain_scores_gemma":[0.9998884,0.00004919367,0.00001923192,0.000006907754,0.00003191592,0.000004358798],"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":[0.00005984934,0.00002783367,0.0001682385,0.00006581086,0.00001542559,0.00006047911,0.00002813361,0.8789093,0.01245684,0.005916241,0.001091786,0.1012002],"study_design_scores_gemma":[0.000004894361,0.00001313828,0.0000630215,0.000003903126,0.000002687437,0.000006280793,0.000001485225,0.9972807,0.0009913276,0.001111293,0.000518534,0.000002648412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02419126,0.001417732,0.9666805,0.0002587741,0.00009815157,0.00003362641,0.00003865871,0.00113101,0.006150261],"genre_scores_gemma":[0.8475119,0.0008900539,0.1383293,0.0001505451,0.00009172276,0.0001778353,0.00008837269,0.00006326503,0.01269697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007222711,"threshold_uncertainty_score":0.01436132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04155367186002447,"score_gpt":0.2894754490523046,"score_spread":0.2479217771922801,"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."}}