{"id":"W4411600109","doi":"10.1109/tro.2025.3582816","title":"To Lead or to Follow? Adaptive Robot Task Planning in Human–Robot Collaboration","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Task (project management); Human–robot interaction; Computer science; Motion planning; Mobile robot; Human–computer interaction; Artificial intelligence; Lead (geology); Engineering; Systems engineering; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002035019,0.0002624865,0.0003343891,0.001015193,0.0003395122,0.0000869209,0.0002223987,0.0002021431,0.001253658],"category_scores_gemma":[0.0000229229,0.0002726302,0.00009780724,0.001554649,0.00003097024,0.0001667727,0.000002951966,0.000437063,0.0009685206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003947704,"about_ca_system_score_gemma":0.0001176099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001253977,"about_ca_topic_score_gemma":0.001488086,"domain_scores_codex":[0.9980703,0.0001896467,0.0006272818,0.0005093786,0.0002296098,0.000373806],"domain_scores_gemma":[0.9989004,0.0002393263,0.00008554853,0.0004448401,0.000185367,0.0001445486],"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":[0.000728158,0.000435566,0.00004867791,0.00001058122,0.000101038,0.00003024724,0.005352403,0.9772607,0.002368507,0.002677071,0.008070956,0.002916084],"study_design_scores_gemma":[0.06241136,0.02415812,0.1219266,0.009294493,0.002357599,0.0002987582,0.2483015,0.285162,0.09951422,0.004065267,0.1276199,0.01489019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007889815,0.00001270683,0.9723987,0.003640161,0.003608836,0.0008289845,0.00003879443,0.0002353498,0.01134668],"genre_scores_gemma":[0.9543465,0.000001721384,0.008531792,0.003364274,0.0000670313,0.000270549,0.000009510883,0.00003551183,0.0333731],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9638669,"threshold_uncertainty_score":0.9999726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05552078824748405,"score_gpt":0.3959729440258783,"score_spread":0.3404521557783943,"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."}}