{"id":"W4407948601","doi":"10.1109/lra.2025.3546106","title":"DR-MPC: Deep Residual Model Predictive Control for Real-World Social Navigation","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Christian Studies; University of Toronto","funders":"","keywords":"Model predictive control; Residual; Control (management); Computer science; Artificial intelligence; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001655971,0.0001331835,0.0001950799,0.0001627823,0.0003220464,0.00007658453,0.00007310374,0.0001130538,0.0000243518],"category_scores_gemma":[0.00002130415,0.0001452444,0.00008078504,0.0001500226,0.00007141502,0.0001309033,0.00000802922,0.0001403591,0.00001438522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001016212,"about_ca_system_score_gemma":0.00003140571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004962648,"about_ca_topic_score_gemma":0.00004021819,"domain_scores_codex":[0.9990396,0.00007737158,0.0003046769,0.0002453958,0.0001234645,0.0002095058],"domain_scores_gemma":[0.9993854,0.0002181906,0.000156391,0.00009864745,0.0001026699,0.00003876505],"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":[0.0006318489,0.0002362511,0.001674228,0.0001142929,0.0006738798,0.000005166594,0.009386731,0.3782628,0.007525621,0.3379448,0.2534212,0.01012313],"study_design_scores_gemma":[0.003202532,0.00006164215,0.02926997,0.00005956111,0.0002256476,0.000001637047,0.0006871701,0.9589957,0.0001946287,0.00581693,0.001199373,0.0002851992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03285604,0.0000162046,0.9297082,0.02985721,0.001856288,0.0006068537,0.00004751707,0.0002116357,0.004840037],"genre_scores_gemma":[0.9889604,0.000003771778,0.001846016,0.006279353,0.0004549181,0.0001491993,0.00006830476,0.0000203584,0.002217716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9561043,"threshold_uncertainty_score":0.5922895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02431091020796528,"score_gpt":0.3480323701422947,"score_spread":0.3237214599343294,"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."}}