{"id":"W1965907282","doi":"10.1109/iros.2005.1545294","title":"Human-robot interaction for robotic grasping: a pilot study","year":2005,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GRASP; Human–computer interaction; Robot; Computer science; Set (abstract data type); Artificial intelligence; Robotic arm; Human–robot interaction; Control (management); Term (time); Simulation; Software 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":[0.00007943474,0.00009271946,0.0001001521,0.00008740956,0.00008562665,0.00004807604,0.00005781732,0.00001532225,0.0002853254],"category_scores_gemma":[0.00001269282,0.00009262866,0.00002759408,0.00007118926,0.000003591541,0.0001761297,0.000009072367,0.00009865256,0.0001215719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005678661,"about_ca_system_score_gemma":0.000002078549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001673541,"about_ca_topic_score_gemma":0.0001683491,"domain_scores_codex":[0.9994956,0.00001215844,0.0001752838,0.0001106688,0.00006740496,0.0001388794],"domain_scores_gemma":[0.9997956,0.00002240805,0.00001853503,0.0001067356,0.00002109807,0.00003557787],"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":[0.000003956115,0.00009808184,0.000518018,0.00001009066,0.00002130964,2.498358e-7,0.0002116438,0.9940708,0.002718237,0.0004020074,0.0008466372,0.001099017],"study_design_scores_gemma":[0.0005969477,0.000483872,0.01739864,0.00000892451,0.000017286,0.000001994743,0.0003849421,0.9793329,0.000202336,0.00002262783,0.00139195,0.0001575641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1126871,0.00002608667,0.874502,0.0001493138,0.0004107764,0.0006186679,1.596107e-8,0.000884409,0.01072164],"genre_scores_gemma":[0.9961557,5.260209e-7,0.001627998,0.00005362738,0.00021183,0.00004297997,0.000003950835,0.00002881723,0.001874566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8834686,"threshold_uncertainty_score":0.3777287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09878151772404854,"score_gpt":0.3263139148579715,"score_spread":0.227532397133923,"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."}}