{"id":"W4399568539","doi":"10.1109/tase.2024.3410297","title":"Sim2Real Rope Cutting With a Surgical Robot Using Vision-Based Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rope; Reinforcement learning; Robot; Artificial intelligence; Computer science; Machine vision; Robot learning; Grippers; Computer vision; Human–computer interaction; Mobile robot; Engineering; Mechanical engineering","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":[0.0002873808,0.0001477375,0.0001014505,0.0004167734,0.0002525204,0.0003557151,0.0000691657,0.00004257793,0.00002472406],"category_scores_gemma":[0.000004404003,0.0001293659,0.00002366055,0.0007046592,0.00004727978,0.0008310915,0.000001100491,0.0002035574,0.000005806762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001393771,"about_ca_system_score_gemma":0.00006187779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008934853,"about_ca_topic_score_gemma":0.000001333399,"domain_scores_codex":[0.9990044,0.000005342376,0.0001745782,0.000219521,0.0003621743,0.0002340033],"domain_scores_gemma":[0.9997035,0.00005347746,0.00001493143,0.00008614024,0.0000528349,0.00008910563],"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":[0.000003585363,0.000004360189,0.000001565176,0.0001466729,0.000007235238,0.000005323083,0.0001481294,0.9853262,0.001645548,0.00001986976,0.000001079896,0.01269048],"study_design_scores_gemma":[0.0001539777,0.00005777927,0.00005016887,0.0002903746,0.00001421099,0.00001883261,0.00002085165,0.9851683,0.01371819,9.278834e-7,0.0003401065,0.0001662408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08208677,0.00003791049,0.9164276,0.00002952985,0.0002335107,0.00009317,6.82351e-7,0.0008829969,0.0002077971],"genre_scores_gemma":[0.9939682,0.00002243672,0.005908901,0.000007361359,0.00002477898,0.00001425139,0.000001278744,0.00002624589,0.00002651096],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9118814,"threshold_uncertainty_score":0.5275387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008952642793106362,"score_gpt":0.2324106717357656,"score_spread":0.2234580289426592,"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."}}