{"id":"W4407826006","doi":"10.1109/lra.2025.3544491","title":"Adaptive Trajectory Learning With Obstacle Awareness for Motion Planning","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Beijing Nova Program; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Foundation for Innovation","keywords":"Obstacle; Trajectory; Motion (physics); Computer science; Artificial intelligence; Computer vision; Physics; Geography","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":[0.0001984308,0.0001379897,0.0001579832,0.0001689309,0.0002948173,0.0001645974,0.0001839207,0.00005019541,2.242076e-7],"category_scores_gemma":[0.00002549294,0.0001289055,0.0000289036,0.00025204,0.00003865248,0.0003416182,0.00002505724,0.000124591,0.000001411808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005283994,"about_ca_system_score_gemma":0.00005102858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008207313,"about_ca_topic_score_gemma":4.144768e-7,"domain_scores_codex":[0.9990796,0.00005370005,0.0001770979,0.0003137785,0.0001537906,0.0002219793],"domain_scores_gemma":[0.9994137,0.0002075783,0.0001112064,0.0001592772,0.00006527993,0.00004294164],"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":[0.000005997643,0.00001251277,0.00157514,0.00003459314,0.00002722387,0.00000645348,0.0006059369,0.9853408,0.000862848,0.002068717,0.0002164998,0.009243272],"study_design_scores_gemma":[0.0004722576,0.00006898199,0.01278491,0.0001578067,0.00001716848,0.000008318629,0.00007280151,0.9854257,0.0006109375,0.0001648042,0.00005839087,0.0001579875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03187424,0.00004650779,0.9655218,0.001599049,0.0004245837,0.0002084251,0.000001251866,0.0002756061,0.00004851903],"genre_scores_gemma":[0.5306388,0.000001264625,0.4686194,0.0005688384,0.0000474144,0.00002856582,0.000007462414,0.00001031154,0.00007795532],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4987646,"threshold_uncertainty_score":0.5256613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02167499159478025,"score_gpt":0.2622859771207763,"score_spread":0.2406109855259961,"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."}}