{"id":"W4416749785","doi":"10.1109/iros60139.2025.11246581","title":"TagGuideBot: Enhancing Robot Intelligence with Object Tags and VLMs","year":2025,"lang":"","type":"article","venue":"","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Robot; Object (grammar); Naturalness; Point (geometry); Motion (physics); Semantics (computer science); Semantic mapping; Visualization; SMT placement equipment","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":[0.0006640871,0.0009714845,0.0005652991,0.0004588663,0.0002769613,0.0008815087,0.001872741,0.0009081275,0.002076295],"category_scores_gemma":[0.001644492,0.0003407294,0.0004517225,0.0003188557,0.0008867499,0.002034685,0.001647986,0.0008855264,0.001171188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004584228,"about_ca_system_score_gemma":0.0009121547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003922656,"about_ca_topic_score_gemma":0.006294753,"domain_scores_codex":[0.9995475,0.0001018964,0.00001532101,0.0001175364,0.0001609864,0.00005664488],"domain_scores_gemma":[0.9994999,0.0002017848,0.00005439666,0.0001179429,0.00007542519,0.00005046491],"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":[0.0006198063,0.0006594536,0.00471428,0.0005199045,0.0001111974,0.000606441,0.001160881,0.09268986,0.160941,0.01802883,0.0141147,0.7058337],"study_design_scores_gemma":[0.00005232004,0.0004837458,0.001113528,0.00003989518,0.00004427527,0.000346438,0.0002100017,0.9018308,0.0650622,0.008378423,0.02236455,0.00007387545],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0460655,0.0004140265,0.9296658,0.0001366541,0.00008006228,0.0001218682,0.0001077545,0.01978843,0.003619859],"genre_scores_gemma":[0.4499762,0.0002130962,0.5415838,0.0003527483,0.00002507482,0.000184212,0.0006142826,0.0007933424,0.006257256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003922656,"threshold_uncertainty_score":0.007799625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009074963396594306,"score_gpt":0.2836824222708823,"score_spread":0.274607458874288,"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."}}