{"id":"W4400678945","doi":"10.1109/tro.2024.3428990","title":"Regret-Based Sampling of Pareto Fronts for Multiobjective Robot Planning Problems","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Regret; Pareto principle; Multi-objective optimization; Robot; Computer science; Sampling (signal processing); Mathematical optimization; Pareto optimal; Motion planning; Artificial intelligence; Mathematics; Machine learning; Computer vision","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":[0.005237808,0.001655073,0.001856139,0.001124069,0.0008708817,0.001310081,0.00180321,0.001556686,0.003015667],"category_scores_gemma":[0.01264614,0.0008915038,0.001066582,0.0009702769,0.001381519,0.001393555,0.001866936,0.002369305,0.0006116192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002175376,"about_ca_system_score_gemma":0.001897035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003844983,"about_ca_topic_score_gemma":0.004972788,"domain_scores_codex":[0.997956,0.001045419,0.00008193053,0.0002054546,0.0005072274,0.0002038066],"domain_scores_gemma":[0.9933152,0.0048125,0.0004544907,0.0004300504,0.000688396,0.000299411],"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":[0.00008024529,0.00004470281,0.0004581949,0.00005477354,0.00002758671,0.00003590243,0.00002788864,0.9725001,0.0005061386,0.008655253,0.0008698967,0.01673939],"study_design_scores_gemma":[0.000007983781,0.00001323644,0.00003826314,0.000005084692,0.000002174189,0.000004859303,0.000003526752,0.9961011,0.0001447933,0.003560684,0.0001162667,0.000002051793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0163613,0.0003936932,0.9800165,0.0002528815,0.00004100132,0.00009597513,0.00005671996,0.0004033512,0.002378429],"genre_scores_gemma":[0.5310456,0.0003809394,0.463917,0.0003825508,0.0001254777,0.0004222589,0.0004451134,0.0002921859,0.002988903],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005237808,"threshold_uncertainty_score":0.02770048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05610988369127533,"score_gpt":0.3238587836040615,"score_spread":0.2677488999127862,"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."}}