首页|Study Data from University of Virginia Provide New Insights into Robotics and Au tomation (Safe Pomdp Online Planning Among Dynamic Agents Via Adaptive Conformal Prediction)
Study Data from University of Virginia Provide New Insights into Robotics and Au tomation (Safe Pomdp Online Planning Among Dynamic Agents Via Adaptive Conformal Prediction)
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 – Investigators discuss new findings in Robotics - Robotics and Automation.According to news reporting originating in C harlottesville, Virginia, by NewsRx journalists, research stated, “Online planni ng for partially observable Markov decision processes (POMDPs) provides efficien t techniques for robot decision-making under uncertainty.However, existing meth ods fall short of preventing safety violations in dynamic environments.”
CharlottesvilleVirginiaUnited StatesNorth and Central AmericaRobotics and AutomationRoboticsUniversity of Vi rginia
NETL
NSTL
