Near-Optimal Belief Space Planning via T-LQG
arXiv:1705.09415
Abstract
We consider the problem of planning under observation and motion uncertainty for nonlinear robotics systems. Determining the optimal solution to this problem, generally formulated as a Partially Observed Markov Decision Process (POMDP), is computationally intractable. We propose a Trajectory-optimized Linear Quadratic Gaussian (T-LQG) approach that leads to quantifiably near-optimal solutions for the POMDP problem. We provide a novel "separation principle" for the design of an optimal nominal open-loop trajectory followed by an optimal feedback control law, which provides a near-optimal feedback control policy for belief space planning problems involving a polynomial order of calculations of minimum order.
3 pages, 3 figures, In Robotics: Science and Systems (RSS) 2017 Workshop of "POMDPs in Robotics: State of The Art, Challenges, and Opportunities"