{"id":"W4380992475","doi":"10.48550/arxiv.2306.08132","title":"Fast-Grasp'D: Dexterous Multi-finger Grasp Generation Through Differentiable Simulation","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; Samsung; Nvidia","keywords":"GRASP; Computer science; Artificial intelligence; Differentiable function; Metric (unit); Computer vision; RGB color model; Displacement (psychology); Engineering; Mathematics","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":[0.0008394736,0.001842567,0.001031535,0.0008604611,0.00047365,0.001234753,0.003054291,0.002228949,0.01073239],"category_scores_gemma":[0.003174352,0.001057571,0.001981033,0.0006559654,0.0009047747,0.001408604,0.002164546,0.001982744,0.004433645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009809018,"about_ca_system_score_gemma":0.001392651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004974498,"about_ca_topic_score_gemma":0.009303066,"domain_scores_codex":[0.9994878,0.0000675095,0.00003737933,0.0001773692,0.0001750904,0.00005481411],"domain_scores_gemma":[0.9989911,0.0004110728,0.00006281956,0.0003692646,0.00009938746,0.00006630742],"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":[0.0005894358,0.0003265716,0.00430106,0.001385337,0.0002813272,0.0003877227,0.000208275,0.7486675,0.02234968,0.007843805,0.05516985,0.1584894],"study_design_scores_gemma":[0.00007374579,0.0001112507,0.0005574052,0.00003914705,0.00001265353,0.0001287713,0.00002199694,0.977566,0.009262428,0.003190944,0.009012744,0.00002287948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1182807,0.001549652,0.7494042,0.0008308697,0.0004894107,0.0005749056,0.01754097,0.09474243,0.01658685],"genre_scores_gemma":[0.4936886,0.0007125027,0.4499934,0.0006463324,0.00005234663,0.001147654,0.03605993,0.007193051,0.0105062],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01073239,"threshold_uncertainty_score":0.03590339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2380168530943126,"score_gpt":0.2225248052560191,"score_spread":0.01549204783829353,"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."}}