首页|Research Conducted at Stanford University Has Provided New Information about Rob otics and Automation (State Estimation and Belief Space Planning Under Epistemic Uncertainty for Learningbased Perception Systems)
Research Conducted at Stanford University Has Provided New Information about Rob otics and Automation (State Estimation and Belief Space Planning Under Epistemic Uncertainty for Learningbased Perception Systems)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Robotics - Ro botics and Automation have been published. According to news reporting from Stan ford, California, by NewsRx journalists, research stated, “Learningbased models for robot perception are known to suffer from two distinct sources of error: al eatoric and epistemic. Aleatoric uncertainty arises from inherently noisy traini ng data and is easily quantified from residual errors during training.” Financial support for this research came from Defense Advanced Research Projects Agency (DARPA). The news correspondents obtained a quote from the research from Stanford Univers ity, “Conversely, epistemic uncertainty arises from a lack of training data, app earing in out-of-distribution operating regimes, and is difficult to quantify. M ost existing state estimation methods handle aleatoric uncertainty through a lea rned noise model, but ignore epistemic uncertainty. In this work, we propose: (i ) an epistemic Kalman filter (EpiKF) to incorporate epistemic uncertainty into s tate estimation with learned perception models, and (ii) an epistemic belief spa ce planner (EpiBSP) that builds on the EpiKF to plan trajectories to avoid areas of high epistemic and aleatoric uncertainty. Our key insight is to train a gene rative model that predicts measurements from states, ‘inverting’ the learned per ception model that predicts states from measurements. We compose these two model s in a sampling scheme to give a well-calibrated online estimate of combined epi stemic and aleatoric uncertainty.”
StanfordCaliforniaUnited StatesNor th and Central AmericaRobotics and AutomationRoboticsStanford University
NETL
NSTL
