{"id":"W2295130897","doi":"10.1109/lra.2016.2528294","title":"Design Principles for a Family of Direct-Drive Legged Robots","year":2016,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":318,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Laboratory; Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Computer science; Robustness (evolution); Transparency (behavior); Legged robot; Simulation; Artificial intelligence; Computer security; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001316862,0.0001206335,0.000189221,0.00007819991,0.00004171159,0.00002239862,0.00006968436,0.00004828809,0.000003742668],"category_scores_gemma":[0.00002038632,0.00009108381,0.00005053555,0.00006034807,0.00003651197,0.0001124349,0.000005994435,0.00002861051,0.000005919012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003062958,"about_ca_system_score_gemma":0.000009527473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001261373,"about_ca_topic_score_gemma":6.725616e-7,"domain_scores_codex":[0.9993191,0.00002597805,0.0002648235,0.0001230382,0.0001030007,0.0001639959],"domain_scores_gemma":[0.9995512,0.000169707,0.00006502635,0.0001242479,0.00004074459,0.00004901027],"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":[0.000004989961,0.000009357474,0.00003366539,0.00003899544,0.00003969441,4.960104e-7,0.00008144054,0.756054,0.23685,0.0007041917,0.0009622832,0.00522089],"study_design_scores_gemma":[0.001598427,0.00005856491,0.003946074,0.000130098,0.00004873779,0.000002451769,0.00001496527,0.9715046,0.02179639,0.0001326521,0.0005097632,0.0002572502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0193473,0.00005008964,0.9780548,0.001554031,0.0003233961,0.0003382924,0.000006478636,0.00019193,0.0001337366],"genre_scores_gemma":[0.9541841,0.00004428976,0.04529473,0.0002633175,0.0000656419,0.00004203933,0.000002110642,0.00002570414,0.00007811717],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9348367,"threshold_uncertainty_score":0.371429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02253485156345155,"score_gpt":0.2141682296624994,"score_spread":0.1916333780990479,"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."}}