{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009581302,0.0026522,0.001690735,0.001483432,0.0007450443,0.00117442,0.003474359,0.001734977,0.005064113],"category_scores_gemma":[0.00235444,0.0006199308,0.0009683343,0.001144669,0.0004847691,0.001867264,0.002022919,0.001701022,0.007201212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001239154,"about_ca_system_score_gemma":0.001592803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0132876,"about_ca_topic_score_gemma":0.0300942,"domain_scores_codex":[0.9984938,0.0001079376,0.00007979595,0.0005960336,0.000539901,0.0001826102],"domain_scores_gemma":[0.9990854,0.0001457084,0.0001102235,0.0002939613,0.0002709563,0.0000938261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001725928,0.0007229109,0.007971477,0.001424472,0.0002530706,0.0005291232,0.0001459161,0.01987818,0.05463244,0.001937444,0.2740138,0.6367654],"study_design_scores_gemma":[0.000401824,0.00115388,0.02483238,0.0003315084,0.0001732588,0.002407815,0.0002729092,0.6548991,0.144059,0.005706721,0.1655459,0.0002157323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1123357,0.006654639,0.5685576,0.0005236351,0.001156048,0.001396274,0.06681406,0.2238102,0.01875191],"genre_scores_gemma":[0.204814,0.001469901,0.4483442,0.0005516186,0.0001502907,0.001024564,0.3187617,0.003011321,0.02187235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0132876,"threshold_uncertainty_score":0.02642053,"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."}}