{"id":"W4385403839","doi":"10.15607/rss.2023.xix.013","title":"PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection","year":2023,"lang":"en","type":"article","venue":"","topic":"Distributed systems and fault tolerance","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology; National Research Foundation of Korea; National Research Foundation","keywords":"Teleoperation; Computer science; Scalability; Robot; Telerobotics; Data collection; Human–computer interaction; Mobile robot; Artificial intelligence; Operating system","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":[],"consensus_categories":[],"category_scores_codex":[0.0003928388,0.0000821619,0.0001167526,0.0001130491,0.0002520547,0.0004077153,0.0007133374,0.00005400468,0.000006836759],"category_scores_gemma":[0.0001175923,0.00007160944,0.00002717756,0.001617871,0.000009586817,0.0009196517,0.0002076957,0.00003759379,0.0001375775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004862916,"about_ca_system_score_gemma":0.0001617778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004231231,"about_ca_topic_score_gemma":0.0001429562,"domain_scores_codex":[0.9989744,0.00003044324,0.0002019457,0.0003878585,0.000165475,0.0002398161],"domain_scores_gemma":[0.9989489,0.00005534898,0.00004751496,0.0007723244,0.0001184974,0.00005744894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001275151,0.0001071656,0.0005109439,0.00006398764,0.0000388368,0.000004741326,0.0001712076,0.001807982,0.004789332,0.09008748,0.7272266,0.175179],"study_design_scores_gemma":[0.0003404752,0.0000375255,0.004936995,0.00001123574,0.000002085046,0.000007038213,0.00002296564,0.9014355,0.0005654671,0.00031363,0.09221496,0.0001121243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008735561,0.00001779349,0.9917027,0.003212829,0.0004545595,0.0003468769,0.00004942706,0.0005158708,0.002826407],"genre_scores_gemma":[0.9105299,0.00003177609,0.05754189,0.0006582319,0.0004852841,0.0002198335,0.00113593,0.00002160408,0.02937555],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9341608,"threshold_uncertainty_score":0.3931608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08226045660615232,"score_gpt":0.3365940579349617,"score_spread":0.2543336013288093,"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."}}