{"id":"W4400245916","doi":"10.1007/s12555-022-0251-0","title":"Expansion of the Workspace of Eye-in-hand Industrial Robots for Robust Hybrid Vision/force Control","year":2024,"lang":"en","type":"article","venue":"International Journal of Control Automation and Systems","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Workspace; Computer science; Robot; Computer vision; Artificial intelligence; Control (management); Industrial robot; Robotic hand; Control engineering; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006307061,0.00008677086,0.0002634678,0.0002454742,0.00002297405,0.0001061926,0.0001394114,0.00006137454,0.0000115933],"category_scores_gemma":[0.0001510438,0.00006208001,0.0001147762,0.00008618027,0.00002481238,0.0002384416,0.000007282088,0.0001551245,5.367394e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005821964,"about_ca_system_score_gemma":0.00004719545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009711505,"about_ca_topic_score_gemma":0.00000331665,"domain_scores_codex":[0.998705,0.00008435282,0.0007166741,0.00006511878,0.0003517876,0.00007704494],"domain_scores_gemma":[0.9990552,0.0002943091,0.0002909845,0.00005834336,0.000269896,0.00003124099],"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":[0.0000947813,0.000009258928,0.001267132,0.00007005261,0.0001360051,0.000003008927,0.0002271048,0.9878228,0.004841554,0.001267124,0.0003870499,0.003874088],"study_design_scores_gemma":[0.002702543,0.00004913751,0.006882506,0.001024324,0.00002920871,0.00003439076,0.0001036474,0.988223,0.0002079438,0.00006438434,0.0006222311,0.00005664451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1481155,0.001727538,0.844629,0.0007988788,0.004150878,0.0003984292,0.00001164006,0.00002794778,0.0001401552],"genre_scores_gemma":[0.9993899,0.00002003991,0.0001484164,0.00001514262,0.0003046303,0.000006026154,0.00000211214,0.00001210962,0.0001015679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8512745,"threshold_uncertainty_score":0.2531549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01874402086910779,"score_gpt":0.2630334706394114,"score_spread":0.2442894497703036,"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."}}