{"id":"W3205968153","doi":"10.1109/icra48506.2021.9562085","title":"Learning-based Inverse Kinematics from Shape as Input for Concentric Tube Continuum Robots","year":2021,"lang":"en","type":"article","venue":"","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inverse kinematics; Kinematics; Concentric; Discretization; Inverse; Artificial intelligence; Computation; Equidistant; Joint (building); Mathematics; Robot; Computer vision; Computer science; Representation (politics); Robot kinematics; Geometry; Algorithm; Mathematical analysis; Engineering; 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":[0.00003686523,0.000111825,0.0001588056,0.00002181708,0.00005493707,0.00005157343,0.0000859576,0.00007054058,0.0007502969],"category_scores_gemma":[0.000141699,0.0001169824,0.00006830203,0.0001859755,0.00001583104,0.00003115024,0.00001909408,0.0001058885,0.0001532731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000281314,"about_ca_system_score_gemma":0.00004025771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001589828,"about_ca_topic_score_gemma":0.00004297451,"domain_scores_codex":[0.999393,0.000008934454,0.0001864438,0.0001489884,0.00008543239,0.0001771931],"domain_scores_gemma":[0.9993371,0.0002782991,0.00002430589,0.0001822542,0.0000976919,0.00008038918],"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":[0.000003053894,0.00005310007,0.001432486,0.00006417504,0.00006655447,0.000006654197,0.0001324534,0.9709505,0.007990571,0.001795961,0.01573453,0.001769898],"study_design_scores_gemma":[0.0006404002,0.00002014374,0.0004252223,0.00001928791,0.00003843928,8.650365e-7,0.000105414,0.9631932,0.01715837,0.0006057119,0.01762185,0.0001710346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3991042,0.0002611095,0.5906233,0.0008845732,0.0004345802,0.0005464915,0.00004013287,0.0008447939,0.007260835],"genre_scores_gemma":[0.9461978,0.00002056227,0.05099001,0.0005847825,0.0001501903,0.00009114855,0.0002815641,0.00005137752,0.001632533],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5470936,"threshold_uncertainty_score":0.8215225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0133982305540907,"score_gpt":0.2315024445148378,"score_spread":0.2181042139607471,"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."}}