{"id":"W4386065097","doi":"10.1109/tro.2023.3303850","title":"Variable Stiffness Soft Robotic Fingers Using Snap-Fit Kinematic Reconfiguration","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kinematics; Stiffness; GRASP; Soft robotics; Robot; Computer science; Engineering; Control reconfiguration; Kinematic chain; Mechanism (biology); Robot kinematics; Degrees of freedom (physics and chemistry); Artificial intelligence; Biomimetics; Variable (mathematics); Control theory (sociology); Mobile robot; Structural engineering; Mathematics; 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":[0.0001935901,0.0005309175,0.0003372157,0.0004290685,0.0002153279,0.0004922572,0.0006150919,0.0004717139,0.001363223],"category_scores_gemma":[0.0004494998,0.0003215913,0.0003509386,0.000200388,0.0005409234,0.0007146979,0.0006033818,0.0003750222,0.0004242963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001606161,"about_ca_system_score_gemma":0.0001383819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009345368,"about_ca_topic_score_gemma":0.0001717694,"domain_scores_codex":[0.9997609,0.00002805095,0.00002299465,0.00006288183,0.0000999488,0.00002515984],"domain_scores_gemma":[0.9995627,0.00007903458,0.0001464685,0.0001381206,0.00002914591,0.00004455334],"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":[0.0001887769,0.00007810402,0.0005265708,0.0001953255,0.00004120631,0.000693252,0.0001406944,0.01825619,0.8898758,0.007234724,0.0003311935,0.08243822],"study_design_scores_gemma":[0.0001784347,0.00272037,0.004462976,0.0001093865,0.000116333,0.004641817,0.0001207102,0.2047319,0.7413036,0.0103624,0.0310014,0.0002508457],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3266034,0.001075851,0.6604567,0.0001287642,0.0001555366,0.0001210516,0.00008415357,0.001966269,0.009408356],"genre_scores_gemma":[0.8893221,0.000182147,0.1071389,0.00004687309,0.00001404937,0.00006061064,0.00003012889,0.00005343996,0.003151776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001363223,"threshold_uncertainty_score":0.004560411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04458002197853674,"score_gpt":0.2594577462299649,"score_spread":0.2148777242514281,"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."}}