{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009579022,0.000372428,0.000315716,0.0002440098,0.0001898786,0.0001280152,0.0002844215,0.0003877063,0.0001402328],"category_scores_gemma":[0.00003013984,0.0004706373,0.000179004,0.0003562759,0.00003207555,0.0003097894,0.000240689,0.0006198567,0.0003970291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002391388,"about_ca_system_score_gemma":0.00002285167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001676417,"about_ca_topic_score_gemma":0.0001716907,"domain_scores_codex":[0.9985554,0.0000878438,0.0002677147,0.0006441667,0.00009900171,0.0003459251],"domain_scores_gemma":[0.9991084,0.00006687597,0.000127401,0.0005147697,0.00009376602,0.00008873675],"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":[0.000006489535,0.00002653265,0.003697691,0.00009592922,0.00009369975,0.0000316084,0.0003054155,0.9937735,0.0004061746,0.001138978,0.000262425,0.0001615394],"study_design_scores_gemma":[0.0004142951,0.00001210012,0.008058026,0.00006726704,0.00009194867,5.710946e-7,0.00006652483,0.9894406,0.0001142667,0.00101367,0.0002594744,0.0004612282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3026459,0.00004289329,0.6948746,0.000008571208,0.0009011585,0.0002337865,0.000003650962,0.000918683,0.0003707604],"genre_scores_gemma":[0.9937195,0.000137091,0.0009897219,0.0000242013,0.0002555425,0.000002554934,0.0003179741,0.0001061188,0.004447343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6938849,"threshold_uncertainty_score":0.9997745,"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."}}