{"id":"W2221547360","doi":"10.1109/mim.2015.7271221","title":"Touch sensing for humanoid robots","year":2015,"lang":"en","type":"article","venue":"IEEE Instrumentation & Measurement Magazine","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Humanoid robot; Robot; Human–computer interaction; Computer science; Artificial intelligence; Perception; Tactile sensor; Variety (cybernetics); Robotics; Engineering; Computer vision","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":[],"consensus_categories":[],"category_scores_codex":[0.0003083183,0.0001910586,0.0001905926,0.00008535803,0.00007165499,0.00007160343,0.0000746451,0.00005079113,0.00001578288],"category_scores_gemma":[0.00007450381,0.0002038701,0.00004419752,0.0001039995,0.00002055477,0.0002723912,0.000007209936,0.00005224458,0.00008365445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002865022,"about_ca_system_score_gemma":0.00003117711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007575079,"about_ca_topic_score_gemma":0.0000329399,"domain_scores_codex":[0.9987677,0.00002631015,0.000343597,0.0001875228,0.0003949447,0.0002798835],"domain_scores_gemma":[0.9993755,0.0000151897,0.00006051871,0.0001656357,0.0002575159,0.0001256623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004111171,0.00001747293,0.00005694422,0.00007433077,0.00004125632,0.000002333245,0.0001967568,0.2664038,0.7104303,0.00009227647,0.008530093,0.01411335],"study_design_scores_gemma":[0.003726297,0.0001802753,0.0006748015,0.0001267931,0.00008832031,0.00002158492,0.0001463687,0.02466335,0.9359593,0.001769063,0.03197586,0.000667963],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6947889,0.0001197438,0.286349,0.0001782808,0.005911001,0.0007282563,0.00002471377,0.001119272,0.01078082],"genre_scores_gemma":[0.9772198,0.000008339846,0.02189064,0.00009306552,0.0003876624,0.00002940134,0.00004184604,0.00005709786,0.0002721157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2824309,"threshold_uncertainty_score":0.831358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09340453637590211,"score_gpt":0.2722196528093393,"score_spread":0.1788151164334372,"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."}}