{"id":"W2967595637","doi":"10.1109/icra.2019.8793961","title":"Learning Motion Planning Policies in Uncertain Environments through Repeated Task Executions","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Task (project management); Robot; Motion planning; Motion (physics); Work (physics); Artificial intelligence; Collision avoidance; Task analysis; Human–computer interaction; Machine learning; Computer security; Collision; Engineering","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":[0.001741954,0.0006763745,0.000466077,0.0003062113,0.0004123924,0.0006562912,0.0009894836,0.0006919084,0.0006420715],"category_scores_gemma":[0.007799602,0.0005186773,0.000310836,0.0003117316,0.00103591,0.001399184,0.0009748855,0.001310079,0.0001575531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007206677,"about_ca_system_score_gemma":0.001240901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004565823,"about_ca_topic_score_gemma":0.006022985,"domain_scores_codex":[0.9991499,0.0003136903,0.00004672588,0.0001821669,0.0001972443,0.000110315],"domain_scores_gemma":[0.9964769,0.002385386,0.0004407432,0.0003448137,0.0002157161,0.000136481],"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":[0.00008014115,0.00004418578,0.0009033308,0.00002437951,0.0000167096,0.00007405274,0.00008700832,0.9663441,0.002025989,0.003058169,0.0002512491,0.02709059],"study_design_scores_gemma":[0.000007805385,0.00002989506,0.0001421938,0.000003497899,0.000003289722,0.0000107459,0.00001849715,0.9938685,0.001023355,0.00467606,0.0002118453,0.000004319403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1593575,0.0001859833,0.8378888,0.0003160937,0.00002669551,0.00007245695,0.00005880139,0.0008100022,0.001283687],"genre_scores_gemma":[0.8850644,0.0001243886,0.1134675,0.000074549,0.0000160045,0.00009069606,0.0001205459,0.00008637853,0.0009554574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004565823,"threshold_uncertainty_score":0.009212494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02332315286623294,"score_gpt":0.2714470973132572,"score_spread":0.2481239444470242,"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."}}