Path Integral Control in Gaussian Belief Space for Partially Observed Systems
For roboticists and control theorists, this work provides a principled method for stochastic optimal control under partial observability with Gaussian beliefs, though it is incremental as it builds on existing PIC and MPPI frameworks.
This paper extends path integral control to partially observed systems by formulating the problem in Gaussian belief space, deriving conditions for the matching condition to hold, and developing the MPPI-Belief algorithm. Experiments on a navigation task show MPPI-Belief outperforms certainty-equivalent and particle-filter-based baselines.
This paper extends path integral control (PIC) to partially observed systems by formulating the problem in Gaussian belief space. PIC relies on the diffusion being proportional to the control channel -- the so-called matching condition -- to linearize the Hamilton-Jacobi-Bellman equation via the Cole-Hopf transform; we show that this condition fails in infinite-dimensional belief space under non-affine observations. Restricting to Gaussian beliefs yields a finite-dimensional approximation with deterministic covariance evolution, reducing the problem to stochastic control of the belief mean. We derive necessary and sufficient conditions for matching in this reduced space, obtain an exact Cole-Hopf linearization with a Feynman-Kac representation, and develop the MPPI-Belief algorithm. Numerical experiments on a navigation task with state-dependent observation noise demonstrate the effectiveness of MPPI-Belief relative to certainty-equivalent and particle-filter-based baselines.