{"id":"W1596400532","doi":"10.1007/978-3-642-15810-0_14","title":"Imitation Learning from Humanoids in a Heterogeneous Setting","year":2010,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Humanoid robot; Imitation; Robot; Task (project management); Human–computer interaction; Artificial intelligence; Computer science; Construct (python library); Social robot; Robot control; Computer vision; Mobile robot; Engineering; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004591285,0.0001484774,0.000154919,0.0007699756,0.0002459993,0.0002604712,0.0005614487,0.0001454007,0.00003147067],"category_scores_gemma":[0.00003989804,0.0001746037,0.00002367536,0.0001732893,0.0002054648,0.001947657,0.0002888037,0.0009246214,0.00005219997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001037182,"about_ca_system_score_gemma":0.00004000822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001749643,"about_ca_topic_score_gemma":0.0000712492,"domain_scores_codex":[0.9990044,0.00002027657,0.000504479,0.0001281617,0.0001951948,0.0001475425],"domain_scores_gemma":[0.9990686,0.0001522994,0.0001240024,0.0005216227,0.00008812665,0.00004530522],"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":[0.000001770656,0.000005379865,0.000778805,0.00003360867,0.000005752493,6.223714e-7,0.007632535,0.7746522,0.0001322367,0.01716222,0.00001756732,0.1995772],"study_design_scores_gemma":[0.0001611224,0.000008923944,0.004227241,0.0001310688,0.00000233129,0.00000369037,0.00003386911,0.9668694,0.00001608231,0.001005242,0.02734503,0.000195968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0224432,0.001409088,0.3536985,0.0004925161,0.001261043,0.001053983,0.000007507095,0.0007901845,0.618844],"genre_scores_gemma":[0.9699941,0.0008308121,0.02847223,0.0001495026,0.00004484594,0.0000151037,0.0001847516,0.00001829126,0.000290409],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9475508,"threshold_uncertainty_score":0.7120132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0342842481827293,"score_gpt":0.2619736285611059,"score_spread":0.2276893803783766,"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."}}