{"id":"W4388407473","doi":"10.1109/tase.2023.3328964","title":"MetaGraspNetV2: All-in-One Dataset Enabling Fast and Reliable Robotic Bin Picking via Object Relationship Reasoning and Dexterous Grasping","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Research Council Canada","keywords":"Computer science; Artificial intelligence; GRASP; Computer vision; Object detection; Segmentation; Leverage (statistics); Robot; Clutter; Task (project management); Object (grammar); Grippers; Bin; Image segmentation; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006983631,0.004959823,0.001796049,0.002413492,0.001048032,0.001870358,0.005223707,0.003933348,0.007359131],"category_scores_gemma":[0.002375275,0.001016934,0.00269031,0.002393564,0.001087996,0.002247664,0.002842064,0.002451163,0.008719661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001464446,"about_ca_system_score_gemma":0.001693025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01682618,"about_ca_topic_score_gemma":0.04437858,"domain_scores_codex":[0.9988781,0.00009653456,0.00005734334,0.0004446255,0.0003653421,0.0001580107],"domain_scores_gemma":[0.9992653,0.0001297748,0.00005771247,0.0002981235,0.0001496231,0.00009962848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001444198,0.001242628,0.008100145,0.006076355,0.0007586991,0.001334991,0.0003219685,0.05865733,0.03455399,0.003317457,0.721709,0.1624833],"study_design_scores_gemma":[0.0008185152,0.001062756,0.03694284,0.001029962,0.0004470889,0.003755538,0.001137797,0.3221845,0.07058848,0.01443621,0.5470465,0.0005497917],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1064168,0.007321397,0.04833656,0.001195734,0.00115385,0.0009225874,0.7442586,0.07088549,0.01950889],"genre_scores_gemma":[0.05396236,0.0006195116,0.03938885,0.0003427045,0.00004784086,0.0004034372,0.9017423,0.00111288,0.002380138],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01682618,"threshold_uncertainty_score":0.0334565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0442660701246327,"score_gpt":0.2637904943419124,"score_spread":0.2195244242172797,"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."}}