{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002824064,0.0004343462,0.0002310696,0.000321174,0.0009737015,0.001376483,0.0006468481,0.000789316,0.001084746],"category_scores_gemma":[0.007461625,0.000269992,0.0002585482,0.0002509216,0.001579376,0.001581297,0.001329216,0.000741779,0.0002345036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004422179,"about_ca_system_score_gemma":0.001104441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001903118,"about_ca_topic_score_gemma":0.001947445,"domain_scores_codex":[0.9975599,0.001634862,0.00006779942,0.0003325605,0.000260767,0.0001440665],"domain_scores_gemma":[0.9970913,0.001672177,0.0004732489,0.0002191506,0.000225741,0.0003184475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001638683,0.0007785465,0.04766129,0.001174653,0.00020455,0.001426696,0.06296965,0.1592783,0.07520917,0.129304,0.003884042,0.5164704],"study_design_scores_gemma":[0.000207899,0.001684781,0.03740664,0.0003158461,0.0001690133,0.001761002,0.04286396,0.5588371,0.03101683,0.2925715,0.03279515,0.0003703558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.344687,0.0005381254,0.6431831,0.0009678058,0.00005025325,0.0001288171,0.00002888613,0.0002319951,0.01018391],"genre_scores_gemma":[0.9328274,0.0001156937,0.06625652,0.000053507,0.000009249744,0.0000689989,0.00001322455,0.00001667406,0.0006387381],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002824064,"threshold_uncertainty_score":0.01493526,"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."}}