{"id":"W2149808338","doi":"10.5194/ms-2-17-2011","title":"A constrained optimization framework for compliant underactuated grasping","year":2011,"lang":"en","type":"article","venue":"Mechanical sciences","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Underactuation; GRASP; Stability (learning theory); Set (abstract data type); Computation; Computer science; Control theory (sociology); Mathematical optimization; Engineering; Control engineering; Robot; Mathematics; Artificial intelligence; Algorithm; Control (management)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001756755,0.001215527,0.00131701,0.0007728651,0.0003680168,0.001244732,0.0009993755,0.0009454173,0.002532158],"category_scores_gemma":[0.002297099,0.0006093517,0.0008160244,0.0006779191,0.001351706,0.000910227,0.001341909,0.000980244,0.0003794685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001179166,"about_ca_system_score_gemma":0.001487097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003789165,"about_ca_topic_score_gemma":0.002936078,"domain_scores_codex":[0.9992756,0.0003518281,0.00002905795,0.0001200348,0.0001747634,0.00004875354],"domain_scores_gemma":[0.9992297,0.000488226,0.00008923493,0.00004370709,0.0001091556,0.00004003504],"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":[0.00001405435,0.00002091039,0.00008044035,0.00006385715,0.00003267662,0.00004270094,0.0000275627,0.9096392,0.0009140997,0.08122592,0.0004222069,0.007516319],"study_design_scores_gemma":[0.000006041354,0.00001573373,0.00002947531,0.000006097069,0.000003476667,0.000004256484,0.000004711418,0.9771709,0.00008935344,0.02207218,0.0005937173,0.00000401653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005583421,0.0004689003,0.9900514,0.000151733,0.0000260879,0.00002922358,0.00004152154,0.00007071057,0.003576947],"genre_scores_gemma":[0.5229661,0.001314321,0.4643728,0.0002180466,0.0001376514,0.0006666182,0.0002627867,0.0002162989,0.009845519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003789165,"threshold_uncertainty_score":0.009290755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1424908992425563,"score_gpt":0.2986349491830751,"score_spread":0.1561440499405188,"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."}}