{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005216746,0.0006998335,0.000502244,0.0005281792,0.0002755609,0.000541823,0.000721634,0.0004665676,0.0008960557],"category_scores_gemma":[0.002731484,0.0003619367,0.0006124314,0.0003798768,0.0007507009,0.001008226,0.0007004604,0.0005886203,0.0001520162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005412532,"about_ca_system_score_gemma":0.00130768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004735198,"about_ca_topic_score_gemma":0.005717243,"domain_scores_codex":[0.9996948,0.00005968162,0.00002182138,0.00007283796,0.0001201489,0.00003079821],"domain_scores_gemma":[0.9987907,0.000693713,0.0001964389,0.0001239124,0.0001398439,0.00005540793],"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":[0.00009767863,0.00002446968,0.0007548567,0.00006611845,0.00002324771,0.0001520225,0.0001135495,0.9433344,0.008354968,0.005070718,0.0002492614,0.0417586],"study_design_scores_gemma":[0.00001089779,0.00005500003,0.0002174055,0.000008930678,0.000006639178,0.00003029085,0.00001547123,0.9882802,0.002880363,0.008205515,0.0002790728,0.00001023312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03807765,0.0001147674,0.9600894,0.00007525936,0.00001145935,0.00003048497,0.00005001268,0.0005126311,0.001038297],"genre_scores_gemma":[0.7446411,0.0001343948,0.2543775,0.00003221616,0.00001131336,0.00008143461,0.0001019747,0.00006925318,0.0005508856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004735198,"threshold_uncertainty_score":0.009415269,"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."}}