{"id":"W2974038530","doi":"10.1109/coase.2019.8843222","title":"Determining Object Properties from Tactile Events During Grasp Failure","year":2019,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"GRASP; Object (grammar); Computer science; Artificial intelligence; Texture (cosmology); Tactile sensor; Computer vision; Surface (topology); Weight distribution; Pattern recognition (psychology); Robot; Image (mathematics); Mathematics; Engineering; Geometry","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00002704644,0.0001110177,0.0001145819,0.00005568428,0.00004686796,0.00003711834,0.00008193091,0.00005146103,0.001328653],"category_scores_gemma":[0.000009683393,0.00009799252,0.00003870717,0.00006513,0.000003398594,0.0002304991,0.00002456154,0.000139561,0.000875305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000316477,"about_ca_system_score_gemma":0.000004769125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000359613,"about_ca_topic_score_gemma":0.00002762752,"domain_scores_codex":[0.9994378,0.00001346222,0.0001327055,0.0001334864,0.0001106741,0.0001718518],"domain_scores_gemma":[0.9997669,0.00001415766,0.00001782418,0.0001508691,0.00001089633,0.00003931417],"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":[0.000008182112,0.000008861627,0.1611347,0.00007944713,0.00007503552,0.000005277681,0.001335406,0.6471,0.1885357,0.00002124327,0.0002178786,0.001478217],"study_design_scores_gemma":[0.0008664179,0.00002619386,0.415315,0.0001706821,0.00001686393,0.000008680103,0.001110332,0.521733,0.05750145,0.00002805801,0.002644195,0.000579122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912606,0.00003865854,0.001098262,0.00001592951,0.0002647191,0.0001043031,1.959314e-7,0.0004819245,0.006735417],"genre_scores_gemma":[0.9967789,0.000003085155,0.0006303179,0.00001819549,0.00007323234,0.000006713547,0.000005262405,0.00003380014,0.00245049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2541803,"threshold_uncertainty_score":0.9999026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01058069066286278,"score_gpt":0.1806366768026334,"score_spread":0.1700559861397706,"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."}}