{"id":"W4393372065","doi":"10.1109/lra.2024.3384037","title":"Learning to Communicate Functional States With Nonverbal Expressions for Improved Human-Robot Collaboration","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Initialization; Robot; Nonverbal communication; Computer science; Human–robot interaction; Process (computing); Speech recognition; Artificial intelligence; Human–computer interaction; Communication; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001640963,0.0008238311,0.0003371808,0.0001967829,0.0002036795,0.000603017,0.0007140519,0.0006088006,0.001450322],"category_scores_gemma":[0.00786189,0.000244822,0.0002676744,0.0001123675,0.0005721044,0.001106539,0.0008603844,0.0006339593,0.0004727805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002485946,"about_ca_system_score_gemma":0.0003844746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004834715,"about_ca_topic_score_gemma":0.0006328537,"domain_scores_codex":[0.9988924,0.000653557,0.00005723041,0.0001682939,0.0001751362,0.00005335921],"domain_scores_gemma":[0.9970856,0.001905902,0.0003782786,0.0002363702,0.0003016982,0.00009212636],"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":[0.0008527953,0.001056261,0.005611752,0.0005690536,0.00009297278,0.0002514099,0.002384044,0.124582,0.3170032,0.003585029,0.001169259,0.5428421],"study_design_scores_gemma":[0.0001664772,0.001842438,0.007643843,0.00007262384,0.0000767207,0.0003547253,0.000553016,0.8549221,0.1214528,0.008582062,0.004223908,0.0001093641],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1442016,0.0001621886,0.8516999,0.0002348156,0.00003159206,0.0001891847,0.00002021761,0.001333391,0.002127159],"genre_scores_gemma":[0.782556,0.00008649581,0.2159711,0.00008894351,0.00001669657,0.0002127836,0.00002561763,0.00007252867,0.0009698113],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001640963,"threshold_uncertainty_score":0.008678317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03451899184064556,"score_gpt":0.2999390871233635,"score_spread":0.265420095282718,"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."}}