{"id":"W7124151028","doi":"10.65109/dzhf5789","title":"Vision-based obstacle run for teams of humanoid robots","year":2011,"lang":"","type":"article","venue":"","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Traverse; Humanoid robot; Robot; Set (abstract data type); Obstacle; Mobile robot; Obstacle avoidance","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":[0.0004182134,0.0005035551,0.0004265058,0.0002464017,0.0009610142,0.0003712288,0.0009343116,0.000744512,0.002905866],"category_scores_gemma":[0.001072428,0.0003291926,0.0003186952,0.0001105424,0.0005600356,0.000526775,0.001783413,0.0007342033,0.0006907241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003125422,"about_ca_system_score_gemma":0.0007345571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005577454,"about_ca_topic_score_gemma":0.004898577,"domain_scores_codex":[0.999822,0.00004213505,0.000006931387,0.00003229415,0.00005911045,0.00003765205],"domain_scores_gemma":[0.9996308,0.0001057642,0.00002700428,0.00004235658,0.00006051247,0.0001335262],"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":[0.00165535,0.0005454731,0.003888353,0.0003628951,0.0001366993,0.002966731,0.002523687,0.5965029,0.1175431,0.0145462,0.01214626,0.2471823],"study_design_scores_gemma":[0.000141316,0.000534974,0.001212833,0.00003696283,0.00002289845,0.0002819187,0.0003410172,0.9679727,0.01212313,0.007866417,0.0094343,0.00003160285],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2699124,0.0004660637,0.7051807,0.0005481965,0.000245599,0.0001810657,0.00008267575,0.004728925,0.01865449],"genre_scores_gemma":[0.8472002,0.0001107578,0.1483436,0.00006618955,0.00001189504,0.0001627117,0.000101003,0.0001012628,0.003902294],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005577454,"threshold_uncertainty_score":0.01108998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0201919405377509,"score_gpt":0.2322854633788047,"score_spread":0.2120935228410538,"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."}}