{"id":"W4414603437","doi":"10.1109/lra.2025.3615522","title":"AORRTC: Almost-Surely Asymptotically Optimal Planning With RRT-Connect","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Satisficing; Asymptotically optimal algorithm; Convergence (economics); Probabilistic logic; Motion planning; Selection (genetic algorithm); Stability theory","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002924677,0.0001804516,0.000185149,0.000161045,0.0002732139,0.0003722973,0.0003020578,0.00007185078,0.000002306257],"category_scores_gemma":[0.00002774397,0.0001576003,0.00003374578,0.0002837388,0.00005385838,0.0003375372,0.00005394621,0.0002002394,0.000009424702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002876388,"about_ca_system_score_gemma":0.00007662531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009884928,"about_ca_topic_score_gemma":6.959588e-7,"domain_scores_codex":[0.9988272,0.00006287296,0.0002375011,0.0003569174,0.000214317,0.0003012398],"domain_scores_gemma":[0.9992287,0.0002697024,0.00009879372,0.0002679086,0.00005550731,0.00007936287],"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":[0.00001241346,0.00002212098,0.003903981,0.00006297796,0.00006351382,0.00005135585,0.0007024407,0.9678552,0.00183517,0.01668327,0.004235899,0.004571628],"study_design_scores_gemma":[0.0005108052,0.0001002895,0.006716248,0.0003618499,0.00002870155,0.00003152499,0.00002035176,0.9902535,0.0007728009,0.0002872318,0.0006130873,0.0003035818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07995319,0.000082853,0.9050853,0.0136931,0.0003056864,0.0001145609,0.00000144851,0.0002957754,0.0004681245],"genre_scores_gemma":[0.7673452,0.000002604291,0.2277461,0.004725638,0.00004890459,0.000007443377,0.000006600645,0.00001081023,0.0001067028],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.687392,"threshold_uncertainty_score":0.6426756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008647812944369643,"score_gpt":0.2327460096369001,"score_spread":0.2240981966925305,"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."}}