{"id":"W3215761265","doi":"10.1109/lra.2023.3242201","title":"Learning to Search in Task and Motion Planning With Streams","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"STREAMS; Task (project management); Motion (physics); Computer science; Artificial intelligence; Economics; Computer network; Management","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":[0.0003286494,0.0001017154,0.0001151971,0.0003186224,0.0001164887,0.0001907854,0.0001216897,0.00003332505,2.144552e-7],"category_scores_gemma":[0.00001999573,0.00009499249,0.000007914496,0.0006026227,0.00002377601,0.0002672338,0.00006050567,0.0001495235,0.00001496499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002759434,"about_ca_system_score_gemma":0.00001271512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002399985,"about_ca_topic_score_gemma":9.439295e-7,"domain_scores_codex":[0.9990402,0.00006081513,0.0001400297,0.0002862716,0.0002220676,0.0002506028],"domain_scores_gemma":[0.999639,0.00009688133,0.00003889401,0.0001232692,0.0000199528,0.00008202378],"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":[0.000001397202,0.000004513573,0.01113629,0.00001529509,0.000004325719,0.00005541363,0.003144931,0.9666438,0.001975332,0.0001083333,0.00009916678,0.01681127],"study_design_scores_gemma":[0.0002128701,0.0000636807,0.09939405,0.00009912067,0.000001974451,0.00001721115,0.0001349619,0.8998095,0.0001197819,0.00002051972,0.0000113019,0.0001150415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4952452,0.000004802991,0.5011954,0.0032222,0.00006961998,0.00008076238,3.473976e-7,0.000170232,0.000011408],"genre_scores_gemma":[0.8996722,0.000003627551,0.09988112,0.000358564,0.00003194017,0.000007006923,0.000005659409,0.00001008738,0.0000298391],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4044269,"threshold_uncertainty_score":0.3873681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01819505755356033,"score_gpt":0.2579148893678889,"score_spread":0.2397198318143286,"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."}}