{"id":"W4404520551","doi":"10.1109/lra.2024.3502066","title":"GraspAgent 1.0: Adversarial Continual Dexterous Grasp Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"GRASP; Adversarial system; Computer science; Artificial intelligence; Human–computer interaction","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001192716,0.001021116,0.0007474351,0.000370119,0.0002268863,0.0006281269,0.001991201,0.001427122,0.005400347],"category_scores_gemma":[0.00239552,0.0005953835,0.0006160268,0.0002462563,0.00083839,0.000882257,0.002020229,0.001961569,0.001262131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008368511,"about_ca_system_score_gemma":0.0007558305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001884882,"about_ca_topic_score_gemma":0.002120524,"domain_scores_codex":[0.9996719,0.00009163692,0.00001586638,0.00007760115,0.0001013317,0.0000416451],"domain_scores_gemma":[0.9994127,0.000303631,0.00005381029,0.0001184078,0.00006866301,0.00004284038],"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":[0.0001165594,0.00006001867,0.0004926986,0.00007865574,0.00005137274,0.00008914249,0.00005940282,0.9131879,0.003996021,0.01376738,0.004075708,0.06402516],"study_design_scores_gemma":[0.000006180352,0.00001921726,0.00003035124,0.000003514386,0.000002492734,0.00001400311,0.000001558034,0.9963233,0.0008920713,0.002021393,0.0006823499,0.000003469943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009688105,0.0001719584,0.9807338,0.0001707592,0.00005108911,0.00008721244,0.0001274768,0.005593746,0.003375921],"genre_scores_gemma":[0.5211506,0.0002896395,0.4644218,0.0004339208,0.00008300377,0.0005472953,0.0006632371,0.001149357,0.01126103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005400347,"threshold_uncertainty_score":0.01806599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00833948835200547,"score_gpt":0.2396735742048696,"score_spread":0.2313340858528641,"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."}}