{"id":"W4310699031","doi":"10.48550/arxiv.1811.04333","title":"Reactive Task and Motion Planning for Robust Whole-Body Dynamic Locomotion in Constrained Environments","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Model-Driven Software Engineering Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; National Science Foundation","keywords":"Correctness; Computer science; Robot; Reachability; Motion planning; Task (project management); Set (abstract data type); Robustness (evolution); Robot locomotion; Planner; Control engineering; Artificial intelligence; Mobile robot; Engineering; Robot control; Theoretical computer science; Algorithm","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":[0.0007130746,0.0005327528,0.0003050457,0.0003380081,0.000455899,0.0007811598,0.001053232,0.0007768795,0.002463379],"category_scores_gemma":[0.001733426,0.0003535795,0.0007935631,0.0002834369,0.001361827,0.001027617,0.001243198,0.001163187,0.0003822567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008578095,"about_ca_system_score_gemma":0.00149891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003680586,"about_ca_topic_score_gemma":0.003920147,"domain_scores_codex":[0.9995284,0.0001096951,0.00002831608,0.00008783115,0.0001975583,0.00004815156],"domain_scores_gemma":[0.9995697,0.0002175541,0.00006498296,0.00007747445,0.00004758818,0.0000227483],"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":[0.0000504402,0.00004591485,0.0003556182,0.0001526024,0.00002786364,0.0001695763,0.0002625211,0.7179275,0.01953812,0.2224111,0.0007304438,0.03832829],"study_design_scores_gemma":[0.00001376068,0.0000328663,0.0000770257,0.00001537687,0.00001063015,0.00003368802,0.00002732612,0.9418156,0.003274259,0.0515036,0.003187856,0.000008004949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006376117,0.00005799889,0.9914984,0.00006478678,0.000007581038,0.00002736754,0.00002363722,0.0002459858,0.001698058],"genre_scores_gemma":[0.3889862,0.0002690765,0.6058424,0.00008341374,0.00001726502,0.0002802415,0.0002128866,0.0001975532,0.004111081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003680586,"threshold_uncertainty_score":0.008240819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04431148033625119,"score_gpt":0.1939266768555433,"score_spread":0.1496151965192921,"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."}}