{"id":"W2754453099","doi":"10.1109/icra.2018.8460730","title":"Learning Sampling Distributions for Robot Motion Planning","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia; National Aeronautics and Space Administration","keywords":"Sampling (signal processing); Motion planning; Computer science; State space; Mathematical optimization; Exploit; Representation (politics); Convergence (economics); Adaptive sampling; Space (punctuation); Artificial intelligence; Robot; Mathematics; Computer vision; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001648007,0.0006738221,0.0008609319,0.0006574516,0.0003999856,0.0007121507,0.001091024,0.0008696089,0.001658327],"category_scores_gemma":[0.008627261,0.0006806026,0.00054833,0.0006800785,0.001408426,0.001650932,0.001192608,0.001839069,0.000288183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001399908,"about_ca_system_score_gemma":0.001139207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004839759,"about_ca_topic_score_gemma":0.004537555,"domain_scores_codex":[0.9993142,0.0002741406,0.00002810899,0.0001518053,0.0001689929,0.00006276502],"domain_scores_gemma":[0.9965011,0.002773794,0.0001968124,0.0002308685,0.0002111776,0.00008620034],"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":[0.00005701692,0.00002968645,0.0005995749,0.00005326552,0.00002216451,0.00002989087,0.00004593504,0.9306831,0.001076252,0.03253696,0.000547393,0.03431881],"study_design_scores_gemma":[0.000005536183,0.00001005882,0.00006157072,0.000004339867,0.000001932182,0.000005588919,0.000002939558,0.9842861,0.0003020043,0.01512708,0.0001898868,0.000002882113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006129283,0.0001949565,0.9928275,0.0001005638,0.00000990625,0.00001942033,0.00003050886,0.0002197725,0.0004680448],"genre_scores_gemma":[0.6937392,0.000782503,0.3020957,0.0001868663,0.0001029283,0.0003133288,0.0003508817,0.0002181464,0.002210393],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004839759,"threshold_uncertainty_score":0.01015705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08737266373194558,"score_gpt":0.3420058092987185,"score_spread":0.2546331455667729,"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."}}