{"id":"W4386136919","doi":"10.1145/3606931","title":"Hierarchical Planning and Control for Box Loco-Manipulation","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ACM on Computer Graphics and Interactive Techniques","topic":"Human Motion and Animation","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Task (project management); Physics engine; Reinforcement learning; Abstraction; Imitation; Control (management); Motion (physics); Artificial intelligence; Architecture; Code (set theory); Virtual actor; Human–computer interaction; Machine learning; Virtual reality; Engineering; Programming language","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":[0.0001348155,0.0000830129,0.0001048557,0.00015882,0.00006789461,0.00005034773,0.0001365866,0.00004313221,6.519753e-7],"category_scores_gemma":[0.00005140373,0.00006423445,0.00003745345,0.0001020511,0.00003660674,0.0001388503,0.00007674813,0.0001240127,2.196495e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008455948,"about_ca_system_score_gemma":0.000001119197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.424282e-7,"about_ca_topic_score_gemma":1.208056e-7,"domain_scores_codex":[0.9996182,0.000002957833,0.0001245792,0.0001062162,0.00006732238,0.000080745],"domain_scores_gemma":[0.9996994,0.00009595379,0.00004897744,0.00005808513,0.00007689974,0.00002065647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005557974,0.0001690575,0.03269494,0.001987431,0.0004649945,0.000001165815,0.007969532,0.0002973958,0.2508165,0.4988277,0.05310355,0.153112],"study_design_scores_gemma":[0.0007243488,0.0006676692,0.104213,0.0009348126,0.00003441545,0.000008839917,0.0001031111,0.6453898,0.104768,0.1373581,0.005461172,0.0003367644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9452982,0.00002494491,0.05250898,0.0006791058,0.000104634,0.0005178059,0.000009281515,0.0005166922,0.0003403833],"genre_scores_gemma":[0.9966556,0.00003028223,0.003064465,0.0001270486,0.0000676267,0.00003372422,0.000002046396,0.00001263808,0.000006601677],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6450924,"threshold_uncertainty_score":0.2619404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02265506890230371,"score_gpt":0.2726919843370266,"score_spread":0.2500369154347228,"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."}}