{"id":"W2909187154","doi":"10.4028/www.scientific.net/amm.886.188","title":"The Development of Supervised Motion Learning and Vision System for Humanoid Robot","year":2019,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Humanoid robot; Artificial intelligence; Robot; Robot control; Computer science; Computer vision; Motion (physics); Convolutional neural network; Mechanism (biology); Social robot; Engineering; Simulation; Mobile robot","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":[0.0004035685,0.0003607694,0.0004255015,0.0003518573,0.0003536755,0.0003030029,0.0007156989,0.0007422281,0.00224998],"category_scores_gemma":[0.0007409299,0.0002651734,0.0003909514,0.000304372,0.0003125443,0.0005506841,0.0003989789,0.0006186441,0.0006501423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000473752,"about_ca_system_score_gemma":0.000904155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00603253,"about_ca_topic_score_gemma":0.00389719,"domain_scores_codex":[0.9997395,0.00003465315,0.00001560606,0.0001059882,0.00007574164,0.00002843247],"domain_scores_gemma":[0.9997568,0.00003741619,0.00002443487,0.0000298001,0.0001317237,0.00001991771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001767941,0.0001796684,0.002238951,0.0001878801,0.00006344269,0.0002471498,0.0001967269,0.06043274,0.09613074,0.004365658,0.005560028,0.8302202],"study_design_scores_gemma":[0.00003680723,0.000325551,0.00311762,0.00002679709,0.00003257057,0.0003197545,0.00004118771,0.9569959,0.02796321,0.002099896,0.009005134,0.00003560229],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02007818,0.0003472604,0.9742694,0.0001697674,0.0001069172,0.0001329339,0.00005393711,0.002086849,0.002754756],"genre_scores_gemma":[0.4450624,0.0004228497,0.5459651,0.0003517115,0.00007610823,0.0004097904,0.0002979476,0.00007538226,0.007338678],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00603253,"threshold_uncertainty_score":0.01199484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01207135891540554,"score_gpt":0.2418546355436433,"score_spread":0.2297832766282377,"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."}}