{"id":"W2290645033","doi":"10.5555/2034396.2034545","title":"Vision-based obstacle run for teams of humanoid robots","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Traverse; Humanoid robot; Robot; Obstacle; Mobile robot; Computer science; Obstacle avoidance; Artificial intelligence; Computer vision; Set (abstract data type); Motion planning; Human–computer interaction; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004508948,0.0005388819,0.0004578869,0.0002486686,0.001019009,0.000404716,0.001007642,0.0008195528,0.003255131],"category_scores_gemma":[0.001223061,0.0003464575,0.0003504944,0.0001207961,0.0005964192,0.000599017,0.001919542,0.0007879609,0.0008464258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003350817,"about_ca_system_score_gemma":0.0008300518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005819849,"about_ca_topic_score_gemma":0.005142999,"domain_scores_codex":[0.9997985,0.00004942351,0.000007884546,0.00003744277,0.00006533268,0.00004155377],"domain_scores_gemma":[0.9995905,0.0001216835,0.00002870669,0.0000493059,0.00006772576,0.0001421057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001984272,0.0005686989,0.004238071,0.0004296618,0.0001487879,0.00356784,0.003124657,0.5493225,0.1198738,0.01696061,0.01563204,0.2841491],"study_design_scores_gemma":[0.0001801231,0.0006029022,0.001335048,0.00004748537,0.00002818351,0.0003749225,0.000467629,0.95961,0.01471467,0.009607242,0.01299224,0.00003965615],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2326815,0.0004701615,0.7412964,0.0006108606,0.0002640444,0.0001957919,0.00009064906,0.0051628,0.01922775],"genre_scores_gemma":[0.8227124,0.0001236006,0.1721738,0.00007737833,0.00001334988,0.0001954151,0.0001187193,0.0001188174,0.004466485],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005819849,"threshold_uncertainty_score":0.01157194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01941562202720969,"score_gpt":0.2188826252284512,"score_spread":0.1994670032012415,"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."}}