{"id":"W4287611755","doi":"","title":"Inferring symbols from demonstrations to support vector-symbolic planning in a robotic assembly task","year":2020,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Brain Institute; University of Waterloo","funders":"","keywords":"Task (project management); Computer science; Human–computer interaction; Artificial intelligence; Robot; Support vector machine; Engineering; Systems engineering","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":[0.0005509833,0.0006025284,0.0006057714,0.0003120167,0.0003949928,0.0006904016,0.0009318481,0.001269622,0.005519415],"category_scores_gemma":[0.006020967,0.0004828235,0.0004199297,0.0005232689,0.0007043875,0.001137569,0.0009077573,0.001539845,0.0004306934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006135005,"about_ca_system_score_gemma":0.001324513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.011273,"about_ca_topic_score_gemma":0.01209049,"domain_scores_codex":[0.9997655,0.00008630507,0.0000151196,0.00005909183,0.00004826119,0.00002579582],"domain_scores_gemma":[0.9967026,0.002821675,0.00009238056,0.0001396447,0.0001454522,0.00009816431],"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":[0.001424602,0.0002570178,0.00125023,0.0004429426,0.00005631899,0.0005480211,0.0005671366,0.6481289,0.02060414,0.01188175,0.003471205,0.3113678],"study_design_scores_gemma":[0.0000268726,0.00005928993,0.0002455098,0.000008909801,0.000008048736,0.00001365058,0.00002962209,0.991418,0.002638081,0.00521541,0.0003308324,0.000005709303],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4213242,0.000528521,0.5674924,0.0009479533,0.0001215946,0.0001543772,0.0005382668,0.003923574,0.004969154],"genre_scores_gemma":[0.8880061,0.0001269541,0.1090197,0.00004652523,0.00001539886,0.00006471214,0.000322751,0.0001317894,0.002266169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.011273,"threshold_uncertainty_score":0.02241474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03109046303050515,"score_gpt":0.2531220880074651,"score_spread":0.22203162497696,"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."}}