{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006324442,0.000191052,0.0001635779,0.0001811881,0.00007943199,0.00008271453,0.00009435284,0.0001343069,0.000114905],"category_scores_gemma":[0.000001669726,0.0001800864,0.0001260397,0.0003472912,0.00003675131,0.00009294725,5.623775e-7,0.000384036,0.0002052449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006419603,"about_ca_system_score_gemma":0.00002240962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002221656,"about_ca_topic_score_gemma":0.00002454933,"domain_scores_codex":[0.9991838,0.00001615063,0.0002238484,0.0001739885,0.0001797964,0.0002223828],"domain_scores_gemma":[0.9995556,0.00007880302,0.000007918658,0.000220152,0.00002418935,0.0001133475],"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":[0.000001740842,0.00003071378,1.369592e-7,0.00009063083,0.00007533317,0.00002796899,0.0001367452,0.9800187,0.000777108,0.01631816,0.0006371498,0.00188556],"study_design_scores_gemma":[0.00009578992,0.00004939898,9.322562e-7,0.0000608866,0.00007280052,0.00001899921,0.00003737614,0.9946683,0.001496019,0.003244932,0.00005563598,0.0001989104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004170734,0.00005427912,0.9948193,0.0002408311,0.002760516,0.0001266159,0.00002313039,0.0007177009,0.000840518],"genre_scores_gemma":[0.1278458,0.0005093861,0.8696379,0.0002625875,0.0002019955,0.00003999832,0.000009784976,0.00015042,0.001342097],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1274287,"threshold_uncertainty_score":0.7343709,"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."}}