{"id":"W4378765280","doi":"10.48550/arxiv.2305.17484","title":"Keep it Upright: Model Predictive Control for Nonprehensile Object Transportation with Obstacle Avoidance on a Mobile Manipulator","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Technische Universität München; University of Toronto","keywords":"Mobile manipulator; Obstacle avoidance; Computer science; Robustness (evolution); Control theory (sociology); Collision avoidance; Robot end effector; Obstacle; Mobile robot; Model predictive control; Task (project management); Robot; Simulation; Collision; Engineering; Artificial intelligence; Control (management)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002289505,0.0005506045,0.0004810168,0.0001771565,0.0003760947,0.0005704486,0.0006762308,0.0005816383,0.001118152],"category_scores_gemma":[0.0004680614,0.0002483314,0.0002900098,0.0002364508,0.0005752011,0.0003766424,0.0006280465,0.0006318571,0.000194592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002794724,"about_ca_system_score_gemma":0.0005486005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00854488,"about_ca_topic_score_gemma":0.006672261,"domain_scores_codex":[0.9999063,0.00001564965,0.000003296295,0.00002213516,0.00003364658,0.00001899533],"domain_scores_gemma":[0.9998626,0.00005790193,0.00003215139,0.00001187774,0.00002467875,0.00001079366],"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":[0.00006094183,0.00002591293,0.0002465925,0.00003915172,0.00001345309,0.0001140955,0.00006053087,0.9752764,0.005805807,0.001958034,0.0002774088,0.01612161],"study_design_scores_gemma":[0.000007710835,0.00004307934,0.000081829,0.000002451077,0.000003212445,0.000005353847,0.000005825541,0.9985385,0.0005688455,0.0005274259,0.0002130386,0.000002672958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.114268,0.0003354895,0.879745,0.0002935026,0.00006336794,0.00004879484,0.00004105393,0.0005669004,0.004637953],"genre_scores_gemma":[0.9757637,0.0001430732,0.02192741,0.00003712527,0.00001741753,0.00006671877,0.00003042423,0.00001742188,0.001996608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00854488,"threshold_uncertainty_score":0.0169903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04648476254631555,"score_gpt":0.1776784881853838,"score_spread":0.1311937256390682,"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."}}