{"id":"W3139089294","doi":"10.5204/thesis.eprints.207886","title":"Robotic grasping in unstructured and dynamic environments","year":2021,"lang":"en","type":"dissertation","venue":"Queensland University of Technology","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Research Council; Australian Centre for Robotic Vision; Amazon Robotics; Australian Government; Canadian Institute for Advanced Research","keywords":"Clutter; Artificial intelligence; Viewpoints; Robotics; Computer science; Computer vision; State (computer science); Robot; Human–computer interaction; Radar; Algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.00001651302,0.0001030038,0.0002198103,0.0003985593,0.00002893235,0.000003135588,0.00008124457,0.0003502447,0.00003914518],"category_scores_gemma":[0.000006134999,0.0001394728,0.00002461142,0.0001534509,0.00003103108,0.00002900197,0.0000177213,0.0002865516,0.000002178141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004730968,"about_ca_system_score_gemma":0.000009073101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003773255,"about_ca_topic_score_gemma":0.001052902,"domain_scores_codex":[0.9996107,0.000009667407,0.00008890648,0.0001400706,0.00004816834,0.0001025335],"domain_scores_gemma":[0.9998236,0.000006869167,0.00004546967,0.000102542,0.00000625185,0.00001531909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00006896484,0.00005013177,0.05956363,0.001330168,0.0003783264,0.0004748155,0.003680008,0.8441685,0.02476589,0.002227527,0.0001516792,0.0631404],"study_design_scores_gemma":[0.002132245,0.00007859884,0.7089301,0.0009145547,0.0001727802,0.00003777296,0.01689872,0.2630444,0.00114184,0.001333006,0.004362697,0.0009532419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936723,0.001195125,0.003144676,0.00006533402,0.0001771872,0.00009761196,4.860752e-7,0.0001067557,0.001540597],"genre_scores_gemma":[0.9971133,0.0005662608,0.0005716986,0.000001147119,0.000002191873,1.034304e-7,0.0001358474,0.0000128758,0.001596592],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6493665,"threshold_uncertainty_score":0.5687537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00312848628640679,"score_gpt":0.1742403279551638,"score_spread":0.171111841668757,"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."}}