{"id":"W4405754403","doi":"10.1109/tro.2024.3521862","title":"Generative Graphical Inverse Kinematics","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Toronto","funders":"European Regional Development Fund; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Kinematics; Inverse kinematics; Inverse; Generative grammar; Computer science; Artificial intelligence; Computer vision; Mathematics; Robot; Geometry; Classical mechanics; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006126986,0.00139153,0.001039871,0.0008037508,0.0004924125,0.001428471,0.001876426,0.001857771,0.009993878],"category_scores_gemma":[0.002688533,0.0009150103,0.001627404,0.0007978886,0.001485685,0.001502052,0.002766608,0.001991285,0.002251339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007726111,"about_ca_system_score_gemma":0.0009479126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002545352,"about_ca_topic_score_gemma":0.004256278,"domain_scores_codex":[0.9995545,0.0001082138,0.00001784193,0.000128542,0.0001398605,0.00005096312],"domain_scores_gemma":[0.9991499,0.0004397957,0.00009283481,0.0001462855,0.0001217767,0.00004950435],"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":[0.00002789706,0.00001867364,0.0003883381,0.0001275218,0.00003577529,0.0001045459,0.00007463241,0.9045556,0.00208728,0.05769087,0.002677146,0.03221176],"study_design_scores_gemma":[0.000008240086,0.00001067401,0.00005612184,0.00001545093,0.000006482921,0.00004531784,0.00001733858,0.9653472,0.0005483985,0.03151109,0.002425706,0.000008024509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0018081,0.0000980984,0.9930259,0.0001285469,0.0000328434,0.00002576886,0.00009199449,0.0005458882,0.004242904],"genre_scores_gemma":[0.3323753,0.0005412712,0.6488182,0.0005128382,0.0001073766,0.0004090088,0.0009210534,0.001101274,0.01521378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009993878,"threshold_uncertainty_score":0.03343284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01424973196948161,"score_gpt":0.2246039983277624,"score_spread":0.2103542663582808,"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."}}