{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002735789,0.0004570555,0.0004866254,0.0003043753,0.0004859672,0.0009710264,0.0004331134,0.0006198708,0.002939152],"category_scores_gemma":[0.0006823133,0.0003458068,0.0003488448,0.0003450724,0.0008677751,0.001160161,0.001123554,0.0005939011,0.001068632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004378133,"about_ca_system_score_gemma":0.0005595832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008176687,"about_ca_topic_score_gemma":0.001106406,"domain_scores_codex":[0.9997655,0.00002224366,0.000007802024,0.00006986838,0.0001056885,0.00002901602],"domain_scores_gemma":[0.9998332,0.00006629602,0.00002583509,0.00003754634,0.00001958793,0.00001751521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001335068,0.0001423972,0.000720264,0.0004619728,0.0000491621,0.0003378876,0.0005483421,0.2742464,0.2002074,0.06855328,0.004371969,0.4502274],"study_design_scores_gemma":[0.0000607546,0.0004102165,0.005529158,0.0001668914,0.00002901785,0.0006909527,0.0004147537,0.7232397,0.07226353,0.1324254,0.06468646,0.00008331424],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1127794,0.003062319,0.8429719,0.0005561531,0.0001070692,0.00009866218,0.00008810642,0.001709523,0.0386268],"genre_scores_gemma":[0.6537605,0.004859153,0.3019873,0.0001622789,0.00008326327,0.0001157641,0.0002344428,0.0002579886,0.03853942],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002939152,"threshold_uncertainty_score":0.009832501,"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."}}