{"id":"W2157492811","doi":"10.1109/robot.2006.1641959","title":"An approach for object manipulation using cooperative agents","year":2006,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Fence (mathematics); Computer science; Flexibility (engineering); Object (grammar); Point (geometry); Orientation (vector space); Human–computer interaction; Task (project management); Computer vision; Position (finance); Artificial intelligence; Simulation; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00005926345,0.00007876057,0.00007212868,0.00005209252,0.00008183002,0.00004984428,0.00003661125,0.00004145731,0.00007378341],"category_scores_gemma":[0.000003215279,0.00007769361,0.00002405232,0.00008399727,0.00000476091,0.000189929,0.000002902655,0.00004193218,0.000005478164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000399124,"about_ca_system_score_gemma":0.000003842983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004847821,"about_ca_topic_score_gemma":0.000008475949,"domain_scores_codex":[0.9995889,0.00001420222,0.000117933,0.0001023977,0.00005979597,0.0001167648],"domain_scores_gemma":[0.9998503,0.000008698156,0.00001328263,0.00007702287,0.00002854564,0.00002210733],"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":[0.000001987318,0.00001130092,0.001213718,0.00001127677,0.000004934673,1.320476e-7,0.00005734718,0.9923585,0.004156963,0.001778043,0.0001894493,0.0002163373],"study_design_scores_gemma":[0.0001920577,0.00001004668,0.01040529,0.000001993176,0.000005947034,0.000001309962,0.00006901059,0.9881493,0.0007935747,0.00003239747,0.000231897,0.0001072137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1026475,0.00001125256,0.8821031,0.000001933342,0.00006350985,0.0001821712,3.102655e-7,0.0002389055,0.0147512],"genre_scores_gemma":[0.9526758,2.967098e-7,0.04664566,0.00001791083,0.0001294021,0.000009689096,0.0001193443,0.00002419002,0.0003776582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8500283,"threshold_uncertainty_score":0.3168253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05770699735729338,"score_gpt":0.2843171340980024,"score_spread":0.226610136740709,"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."}}