{"id":"W4414902561","doi":"10.48550/arxiv.2503.15009","title":"Modeling, Embedded Control and Design of Soft Robots using a Learned Condensed FEM Model","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Willow Biosciences (Canada)","funders":"","keywords":"Finite element method; Robot; Actuator; Embedding; Computation; Adaptability; Kinematics; Robotics","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":[0.0003262992,0.0004935189,0.000500121,0.0003487608,0.0001616142,0.0005209186,0.0005420282,0.0006626251,0.001377647],"category_scores_gemma":[0.0007635491,0.0003376228,0.0005642925,0.0002037065,0.0007679343,0.0006315804,0.0008598787,0.0006949893,0.0003345473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003759461,"about_ca_system_score_gemma":0.0006176399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001189742,"about_ca_topic_score_gemma":0.001196012,"domain_scores_codex":[0.9998185,0.00003682085,0.000007673314,0.00003504505,0.00008786938,0.00001423671],"domain_scores_gemma":[0.9997446,0.00008806623,0.00004651328,0.00005611749,0.00005034289,0.00001441255],"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":[0.00001169584,0.000008505124,0.0001489933,0.00002745943,0.000007239024,0.00002307641,0.00002533583,0.9768139,0.005432506,0.007716003,0.0001381504,0.009647192],"study_design_scores_gemma":[0.000001552943,0.00001198772,0.00004472608,0.000003167997,0.000001498797,0.000005601126,0.000002649633,0.997023,0.0005820704,0.001910008,0.0004119033,0.000001865066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007175062,0.00006072954,0.9910165,0.00004944547,0.00001110556,0.00001648748,0.00002325812,0.0001352362,0.001512176],"genre_scores_gemma":[0.7420352,0.0004420985,0.2510845,0.00009123062,0.00003376187,0.0003041814,0.0001812032,0.0001143261,0.005713501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001377647,"threshold_uncertainty_score":0.004608691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1044192141922133,"score_gpt":0.2968832616867623,"score_spread":0.192464047494549,"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."}}