{"id":"W4380136701","doi":"10.48550/arxiv.2306.05262","title":"EXOT: Exit-aware Object Tracker for Safe Robotic Manipulation of Moving Object","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Seoul National University","keywords":"Artificial intelligence; Computer vision; Computer science; BitTorrent tracker; Robot; Object (grammar); Minimum bounding box; Benchmark (surveying); Video tracking; Classifier (UML); Eye tracking; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002207178,0.0003395089,0.0004589592,0.0006850768,0.0001125947,0.00004046819,0.0003367482,0.0003731412,0.00009857267],"category_scores_gemma":[0.00006270528,0.000443251,0.0002962062,0.0005821101,0.00003742437,0.0001895618,0.0001789374,0.0004834299,0.0001004025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001904623,"about_ca_system_score_gemma":0.00004719849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009892145,"about_ca_topic_score_gemma":0.0001149863,"domain_scores_codex":[0.9985952,0.00006477303,0.0003527972,0.0005552053,0.00009554777,0.0003364164],"domain_scores_gemma":[0.9989015,0.000189181,0.0001966037,0.0005020992,0.000121257,0.00008938485],"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":[0.00002453353,0.00001448546,0.003438892,0.0006131286,0.0001296681,0.00002496591,0.0002012008,0.9928084,0.0001150045,0.002175642,0.0003126062,0.0001414868],"study_design_scores_gemma":[0.000428839,0.00002496204,0.02143718,0.0002424211,0.0001493195,0.000001447707,0.0002459992,0.9745613,0.0001269725,0.00227119,0.00008854468,0.0004217698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1883208,0.00006747843,0.8069896,0.00001939028,0.001169673,0.0007051794,0.0000110397,0.0008233072,0.001893542],"genre_scores_gemma":[0.996428,0.0001271124,0.0006127973,0.000008506128,0.0001342626,0.000003724079,0.0001832605,0.0001101663,0.00239221],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8081071,"threshold_uncertainty_score":0.9998019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1415021005695992,"score_gpt":0.2120631040929982,"score_spread":0.07056100352339903,"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."}}