{"id":"W2001487393","doi":"10.1155/2009/984752","title":"Vector Field Driven Design for Lightweight Signal Processing and Control Schemes for Autonomous Robotic Navigation","year":2009,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Neuromorphic engineering; Robotics; Artificial intelligence; Field (mathematics); Exploit; Signal processing; SIGNAL (programming language); Computer architecture; Control engineering; Robot; Human–computer interaction; Digital signal processing; Computer hardware; Artificial neural network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006003787,0.000327903,0.000239574,0.0002706471,0.000264055,0.0005633485,0.0005384283,0.0004661861,0.002577947],"category_scores_gemma":[0.000920514,0.0001833124,0.0002918313,0.000206556,0.0006225312,0.0007229401,0.0005318114,0.0005726148,0.0003504772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005128696,"about_ca_system_score_gemma":0.0004289095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003092731,"about_ca_topic_score_gemma":0.0004344683,"domain_scores_codex":[0.9998201,0.0000484628,0.00001163389,0.00002187917,0.00008336267,0.0000145925],"domain_scores_gemma":[0.9997768,0.00009370846,0.00003847589,0.00002993295,0.00004614848,0.00001499677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006378329,0.00006180745,0.0002378545,0.0001910034,0.00001954347,0.00008913166,0.0001790864,0.3191473,0.03844903,0.5581592,0.001249931,0.0821523],"study_design_scores_gemma":[0.00003373693,0.0001129788,0.00005771338,0.00001964052,0.000006635687,0.00004055442,0.00001544802,0.9086676,0.004967377,0.07916687,0.006898325,0.00001309714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006799293,0.00008301948,0.9904572,0.0001152523,0.00002801147,0.0000340221,0.000007990255,0.00008497916,0.002390223],"genre_scores_gemma":[0.5335156,0.0003092169,0.4596948,0.0001496664,0.00005279872,0.0003009786,0.00004008958,0.00007142624,0.005865305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002577947,"threshold_uncertainty_score":0.008624136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01933417197611955,"score_gpt":0.2952463482339154,"score_spread":0.2759121762577959,"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."}}