Advancing Trajectory Optimization with Approximate Inference: Exploration, Covariance Control and Adaptive Risk

by   Joe Watson, et al.

Discrete-time stochastic optimal control remains a challenging problem for general, nonlinear systems under significant uncertainty, with practical solvers typically relying on the certainty equivalence assumption, replanning and/or extensive regularization. Control as inference is an approach that frames stochastic control as an equivalent inference problem, and has demonstrated desirable qualities over existing methods, namely in exploration and regularization. We look specifically at the input inference for control (i2c) algorithm, and derive three key characteristics that enable advanced trajectory optimization: An `expert' linear Gaussian controller that combines the benefits of open-loop optima and closed-loop variance reduction when optimizing for nonlinear systems, inherent adaptive risk sensitivity from the inference formulation, and covariance control functionality with only a minor algorithmic adjustment.


page 1

page 2

page 3

page 4


Decoupled Data Based Approach for Learning to Control Nonlinear Dynamical Systems

This paper addresses the problem of learning the optimal control policy ...

Stochastic Control through Approximate Bayesian Input Inference

Optimal control under uncertainty is a prevailing challenge in control, ...

Structure-preserving constrained optimal trajectory planning of a wheeled inverted pendulum

The Wheeled Inverted Pendulum (WIP) is an underactuated, nonholonomic me...

Stochastic Optimal Control as Approximate Input Inference

Optimal control of stochastic nonlinear dynamical systems is a major cha...

RAT iLQR: A Risk Auto-Tuning Controller to Optimally Account for Stochastic Model Mismatch

Successful robotic operation in stochastic environments relies on accura...

Active Inference or Control as Inference? A Unifying View

Active inference (AI) is a persuasive theoretical framework from computa...

Active Exploration and Mapping via Iterative Covariance Regulation over Continuous SE(3) Trajectories

This paper develops iterative Covariance Regulation (iCR), a novel metho...

Please sign up or login with your details

Forgot password? Click here to reset