{"id":"W3111827785","doi":"10.1109/syscon47679.2020.9275929","title":"Grasp Selection for In-Hand Robotic Manipulation of Non-Rigid Objects with Shape Control","year":2020,"lang":"en","type":"article","venue":"2020 IEEE International Systems Conference (SysCon)","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GRASP; Computer science; Grippers; Object (grammar); Artificial intelligence; Computer vision; Task (project management); Selection (genetic algorithm); Robotic hand; Control (management); Robotic arm; Robot; Human–computer interaction; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001192329,0.000181252,0.000324864,0.0001244119,0.0000401859,0.0001104574,0.0001663192,0.00008927993,0.00009105376],"category_scores_gemma":[0.00004745493,0.0001792475,0.00005464852,0.0002057797,0.00002318547,0.0002799223,0.000007059286,0.0001612499,0.00002798784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009523401,"about_ca_system_score_gemma":0.0000531422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00011241,"about_ca_topic_score_gemma":0.0001503425,"domain_scores_codex":[0.9987981,0.0000363316,0.0004703504,0.0002447202,0.0002705006,0.0001800461],"domain_scores_gemma":[0.9993352,0.00008858904,0.0001626458,0.0000749327,0.0002618827,0.00007672935],"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":[0.00009789602,0.00001272695,0.01600811,0.0002094803,0.0000997376,0.00000266694,0.0005098504,0.9532475,0.02834889,0.001047744,0.0001123402,0.0003030534],"study_design_scores_gemma":[0.001426189,0.0001284668,0.01875608,0.0002106067,0.00002118595,0.000009129653,0.0001706515,0.9776567,0.001232563,0.00001563494,0.0001867294,0.0001860176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2308623,0.00004004167,0.7651872,0.0002466305,0.0009999361,0.0008671372,0.000006376202,0.0001206096,0.001669704],"genre_scores_gemma":[0.9990549,0.000006324809,0.0002969937,0.00004960191,0.0003139673,0.00009391484,0.00004223531,0.00003311443,0.0001089685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7681925,"threshold_uncertainty_score":0.7309501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03307926620430285,"score_gpt":0.2497327856870193,"score_spread":0.2166535194827164,"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."}}