{"id":"W3127820998","doi":"10.1007/978-3-030-66723-8_23","title":"Learning Control Sets for Lattice Planners from User Preferences","year":2021,"lang":"en","type":"book-chapter","venue":"Springer proceedings in advanced robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Planner; Computer science; Lattice (music); Set (abstract data type); Mathematical optimization; Robot; Motion planning; Trajectory; Function (biology); Artificial intelligence; Mathematics","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":[0.002897862,0.00108024,0.001467739,0.001305969,0.0005151142,0.002119427,0.002279064,0.001305068,0.006772256],"category_scores_gemma":[0.01271543,0.001509315,0.001297724,0.001361773,0.00172825,0.004188914,0.002897229,0.003636966,0.0009805312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001694678,"about_ca_system_score_gemma":0.001540772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004726015,"about_ca_topic_score_gemma":0.006281062,"domain_scores_codex":[0.9980526,0.0007117032,0.0001341247,0.0004246914,0.0005298936,0.000146994],"domain_scores_gemma":[0.9935572,0.005038351,0.0002966807,0.000522103,0.0003983756,0.0001873324],"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":[0.0002098305,0.0001438971,0.0004788461,0.0001578272,0.00005288888,0.00004437063,0.0002016097,0.7317449,0.001496365,0.05578517,0.002689534,0.2069947],"study_design_scores_gemma":[0.00001290616,0.00002888268,0.00004730409,0.00001470086,0.000003846123,0.000007475549,0.00001649175,0.9498823,0.0004098406,0.04920314,0.0003653596,0.000007822967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007355164,0.000131952,0.9899057,0.0001023846,0.000021542,0.0000599866,0.00009622514,0.0004340925,0.001892988],"genre_scores_gemma":[0.521714,0.0005147359,0.4688001,0.000182568,0.0000776895,0.0008297306,0.0009823987,0.0005241124,0.006374625],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006772256,"threshold_uncertainty_score":0.02265543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02520290092323937,"score_gpt":0.2620186792241462,"score_spread":0.2368157783009069,"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."}}