{"id":"W2923164151","doi":"10.48550/arxiv.1903.09189","title":"Long range teleoperation for fine manipulation tasks under time-delay network conditions","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Teleoperation; Computer science; Telerobotics; Latency (audio); Interface (matter); Range (aeronautics); Task (project management); Computer vision; Artificial intelligence; Controller (irrigation); Robot; Real-time computing; Simulation; Key (lock); Mobile robot; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000103944,0.0002545036,0.0002596232,0.0001394168,0.0001341267,0.00006578238,0.0001654356,0.0003543807,0.0001415036],"category_scores_gemma":[0.000009602332,0.0003218089,0.0001445394,0.000218646,0.00002605389,0.0001514494,0.00006439533,0.0002399352,0.0001435577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002514832,"about_ca_system_score_gemma":0.00004540892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002043034,"about_ca_topic_score_gemma":0.00009117807,"domain_scores_codex":[0.9990382,0.00004119797,0.0002073744,0.0003993731,0.00005753558,0.0002563519],"domain_scores_gemma":[0.9992164,0.0000864707,0.00008577562,0.000394872,0.0001465761,0.00006989057],"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":[0.00001846699,0.00001741828,0.001390717,0.0001171837,0.00009122544,0.000007978925,0.00001766842,0.9829528,0.0000426061,0.01213204,0.00318193,0.00002995291],"study_design_scores_gemma":[0.0005518788,0.00002617242,0.002185446,0.00008134381,0.0001566457,0.000001222765,0.000009782011,0.9917701,0.00003624144,0.004597587,0.0002301042,0.0003534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1389352,0.00005400061,0.8584253,0.00003077054,0.0006664729,0.0007219617,0.00008420104,0.0002519297,0.0008302281],"genre_scores_gemma":[0.9949428,0.00006070553,0.0004445031,0.0000414558,0.0002678511,0.000003423141,0.002431179,0.00006189146,0.001746123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8579807,"threshold_uncertainty_score":0.9999234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0479608178914243,"score_gpt":0.1816146237296074,"score_spread":0.1336538058381831,"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."}}