Kavli Affiliate: Ran Wang
| First 5 Authors: Ran Wang, Raman Goyal, Suman Chakravorty, ,
| Summary:
This paper studies the learning-to-control problem under process and sensing
uncertainties for dynamical systems. In our previous work, we developed a
data-based generalization of the iterative linear quadratic regulator (iLQR) to
design closed-loop feedback control for high-dimensional dynamical systems with
partial state observation. This method required perfect simulation rollouts
which are not realistic in real applications. In this work, we briefly
introduce this method and explore its efficacy under process and sensing
uncertainties. We prove that in the fully observed case where the system
dynamics are corrupted with noise but the measurements are perfect, it still
converges to the global minimum. However, in the partially observed case where
both process and measurement noise exist in the system, this method converges
to a biased "optimum". Thus multiple rollouts need to be averaged to retrieve
the true optimum. The analysis is verified in two nonlinear robotic examples
simulated in the above cases.
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