{"id":"W2963945072","doi":"10.1109/lra.2019.2931221","title":"Haptic Interface for Handshake Emulation","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Handshake; Haptic technology; Trajectory; Interface (matter); Impedance control; Simulation; Hexapod; Computer science; Stiffness; Emulation; Robot; Control theory (sociology); Engineering; Control (management); Artificial intelligence; Asynchronous communication; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005080993,0.0005165996,0.000376899,0.0001984482,0.0001490947,0.0004911923,0.0009233927,0.000965576,0.01195866],"category_scores_gemma":[0.001666685,0.0001470739,0.0002506117,0.00009565915,0.0003244006,0.0006380901,0.0006442975,0.0004937316,0.001651825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001678855,"about_ca_system_score_gemma":0.0001003507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001071928,"about_ca_topic_score_gemma":0.00009140401,"domain_scores_codex":[0.9996148,0.00009950596,0.00002042549,0.0000464041,0.0001906211,0.00002814111],"domain_scores_gemma":[0.9995174,0.0002378273,0.0000296989,0.0001011911,0.00008726379,0.00002671699],"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":[0.0008381385,0.0002307421,0.0002888403,0.0004814544,0.00003980209,0.0007238786,0.0002950107,0.007318784,0.7362777,0.009577376,0.005169399,0.2387589],"study_design_scores_gemma":[0.0004785284,0.003862147,0.003871001,0.0001904322,0.00008351114,0.00551428,0.000139771,0.3394566,0.4929264,0.008060423,0.1452442,0.000172694],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03338222,0.0004559898,0.9542274,0.0002263681,0.0003105532,0.0001926634,0.00007082834,0.003763417,0.007370561],"genre_scores_gemma":[0.6837893,0.0003690977,0.2977132,0.0004642267,0.0001335645,0.0003134763,0.0001789083,0.0003099542,0.01672832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01195866,"threshold_uncertainty_score":0.04000568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0233205542543973,"score_gpt":0.255150766532253,"score_spread":0.2318302122778557,"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."}}