{"id":"W2802892321","doi":"10.1177/0278364918772024","title":"Active sensing for motion planning in uncertain environments via mutual information policies","year":2018,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Scalability; Motion planning; A priori and a posteriori; Computer science; Graph; Path (computing); Mathematical optimization; Enhanced Data Rates for GSM Evolution; Upper and lower bounds; Artificial intelligence; Theoretical computer science; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003000553,0.001429402,0.00138447,0.00105815,0.0007304369,0.0014136,0.001466756,0.001315154,0.001866471],"category_scores_gemma":[0.008146703,0.0008179215,0.0008628322,0.001017374,0.002477017,0.002316111,0.002337897,0.001844856,0.0002621798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001941619,"about_ca_system_score_gemma":0.001908735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002928454,"about_ca_topic_score_gemma":0.002287784,"domain_scores_codex":[0.9980965,0.0008834235,0.00008039686,0.0002682146,0.000478275,0.0001931065],"domain_scores_gemma":[0.9935874,0.005237145,0.0005219688,0.0002233683,0.0002712577,0.0001588119],"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":[0.00006421623,0.00002577133,0.0001643567,0.00004756265,0.00002171434,0.00003699142,0.00006659453,0.9637637,0.0004036969,0.02646603,0.0003598969,0.008579505],"study_design_scores_gemma":[0.00000878651,0.00002097088,0.00003210706,0.000006359106,0.000003637574,0.00000732903,0.000007065804,0.9823648,0.000234272,0.01705935,0.0002510906,0.000004218122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01177405,0.0002779372,0.9850475,0.0002689285,0.00002345295,0.00004295235,0.00004009812,0.0002161666,0.002308961],"genre_scores_gemma":[0.8456886,0.0004298087,0.1508559,0.0001494251,0.00007363434,0.0003362343,0.0001214435,0.00009585191,0.002249022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003000553,"threshold_uncertainty_score":0.01586866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1078908325329916,"score_gpt":0.4120741131170773,"score_spread":0.3041832805840857,"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."}}