{"id":"W2010788624","doi":"10.1142/s0219843609001826","title":"MOTION PLANNING USING PREDICTED PERCEPTIVE CAPABILITY","year":2009,"lang":"en","type":"article","venue":"International Journal of Humanoid Robotics","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; Japan Society for the Promotion of Science; American Academy of Arts and Sciences; National Aeronautics and Space Administration; National Science Foundation","keywords":"Computer science; Planner; GRASP; Humanoid robot; Task (project management); Robot; Perception; Artificial intelligence; Process (computing); Human–computer interaction; Computer vision; Motion planning; Metric (unit); Motion (physics); Simulation; Systems engineering","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":[0.0002375751,0.00009734741,0.0001379018,0.0002175193,0.00008174695,0.0001411355,0.0007853499,0.00003216578,0.00001601681],"category_scores_gemma":[0.0001274306,0.00008688859,0.00009373525,0.0001033822,0.00003461407,0.001084657,0.00007923587,0.0002362835,0.000002702976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001886588,"about_ca_system_score_gemma":0.00006979755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001225081,"about_ca_topic_score_gemma":9.766691e-8,"domain_scores_codex":[0.9987125,0.00004694321,0.0004216142,0.0001377647,0.0005471237,0.000134021],"domain_scores_gemma":[0.9986389,0.00003990082,0.0003531732,0.0001399756,0.00075246,0.00007559727],"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":[0.00006991399,0.000310622,0.003668808,0.000004858211,0.0001000559,0.0002976618,0.004140191,0.8534721,0.02757302,0.01388048,0.0005680221,0.09591424],"study_design_scores_gemma":[0.0008797933,0.0002458561,0.04097232,0.0001897575,0.00001587522,0.0006154539,0.0002048998,0.9297386,0.001773731,0.02491226,0.0002483738,0.0002031141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04737798,0.00006527396,0.9500693,0.00102377,0.001051289,0.0000347761,0.000001060125,0.00002860562,0.000347968],"genre_scores_gemma":[0.7279575,0.000006909249,0.2714023,0.000344741,0.0002614735,6.177314e-8,9.586729e-7,0.000003418664,0.00002253544],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6805796,"threshold_uncertainty_score":0.3543214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03381793460602259,"score_gpt":0.3456497361763989,"score_spread":0.3118318015703763,"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."}}