{"id":"W1797780395","doi":"10.1109/robot.1999.773980","title":"Part orienting with a force/torque sensor","year":2003,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Testbed; Torque; Orientation (vector space); GRASP; Rotation (mathematics); Process (computing); Computer science; Enhanced Data Rates for GSM Evolution; Computer vision; Artificial intelligence; Control theory (sociology); Simulation; Physics; Mathematics; Control (management); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001655514,0.0003588013,0.0003229188,0.0003623302,0.0001838868,0.0004416363,0.0004490718,0.0003193007,0.001653268],"category_scores_gemma":[0.0007304975,0.0002500854,0.0002307257,0.0002816906,0.0003345062,0.0006171336,0.0002508106,0.0002276086,0.0004766972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000245052,"about_ca_system_score_gemma":0.0002823214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008317509,"about_ca_topic_score_gemma":0.001253844,"domain_scores_codex":[0.9998083,0.00001693327,0.000007161102,0.00003687409,0.0001164163,0.00001438142],"domain_scores_gemma":[0.9997801,0.00005257348,0.00004220468,0.0000481816,0.00005916211,0.00001782835],"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":[0.00026384,0.00006022121,0.001443773,0.0001178367,0.0000237556,0.0001218951,0.00009250891,0.04835283,0.5232076,0.003403333,0.001075673,0.4218367],"study_design_scores_gemma":[0.00003237398,0.0003107521,0.00434945,0.00002245887,0.00005026503,0.0005595271,0.00005886449,0.4422521,0.5328982,0.001878876,0.01752358,0.00006349419],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05339793,0.0001740563,0.9416882,0.00005132853,0.00006393851,0.00003659627,0.00003415134,0.00134645,0.003207373],"genre_scores_gemma":[0.4860784,0.0002547065,0.5112562,0.00004507149,0.00002387969,0.00002874373,0.00008682488,0.0001140082,0.002112234],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001653268,"threshold_uncertainty_score":0.005530715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01181762480602776,"score_gpt":0.1882116508869303,"score_spread":0.1763940260809025,"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."}}