Near-Optimal Belief Space Planning via T-LQG

TL;DR

This paper introduces a T-LQG method for belief space planning in nonlinear robotics, achieving near-optimal solutions to complex POMDP problems with reduced computational complexity.

Contribution

It presents a novel separation principle that simplifies belief space planning by decoupling trajectory design from feedback control, enabling near-optimal solutions.

Findings

T-LQG achieves near-optimality in belief space planning.

The separation principle simplifies complex POMDP solutions.

The method reduces computational complexity for nonlinear systems.

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.