{"id":"W2903857705","doi":"10.1109/lra.2018.2885584","title":"Robot Cooperative Behavior Learning Using Single-Shot Learning From Demonstration and Parallel Hidden Markov Models","year":2018,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation","keywords":"Robot; Computer science; Artificial intelligence; Robustness (evolution); Hidden Markov model; Human–computer interaction; Task (project management); Robot learning; Machine learning; Mobile robot; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009533905,0.0006186079,0.0007362387,0.000329031,0.0003232777,0.0004030587,0.001385014,0.0008473974,0.0009163208],"category_scores_gemma":[0.002778047,0.0006063033,0.0006510504,0.0002504603,0.0006765714,0.001017533,0.001097249,0.001326311,0.0002433089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007958683,"about_ca_system_score_gemma":0.0009478285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006996627,"about_ca_topic_score_gemma":0.006852638,"domain_scores_codex":[0.9996093,0.000123713,0.00001911466,0.0001100355,0.00008645081,0.00005146921],"domain_scores_gemma":[0.9985387,0.0008423856,0.0001770879,0.000191874,0.0001592551,0.00009062848],"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":[0.000181118,0.0001624204,0.001506305,0.00007266078,0.00007606872,0.0001285744,0.0001568618,0.8760269,0.005803872,0.004160494,0.0005221193,0.1112027],"study_design_scores_gemma":[0.00000397761,0.00002872909,0.00009737789,0.000001534667,0.000002994831,0.000007606675,0.000003143154,0.9981254,0.0005180519,0.001156933,0.00005084194,0.000003325916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06423692,0.0002177658,0.9332859,0.0001737976,0.00002432442,0.0000467271,0.00002722159,0.0007963814,0.00119082],"genre_scores_gemma":[0.9244986,0.0001115459,0.07325164,0.00008747859,0.00001545708,0.0001020895,0.00007555711,0.00003832069,0.001819364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006996627,"threshold_uncertainty_score":0.01391178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04922757461608525,"score_gpt":0.2465517660584172,"score_spread":0.1973241914423319,"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."}}