# Robust Control Under Uncertainty via Bounded Rationality and Differential Privacy

@article{Pacelli2022RobustCU, title={Robust Control Under Uncertainty via Bounded Rationality and Differential Privacy}, author={Vincent Pacelli and Anirudha Majumdar}, journal={2022 International Conference on Robotics and Automation (ICRA)}, year={2022}, pages={3467-3474} }

The rapid development of affordable and compact high-fidelity sensors (e.g., cameras and LIDAR) allows robots to construct detailed estimates of their states and environments. However, the availability of such rich sensor information introduces two challenges: (i) the lack of analytic sensing models, which makes it difficult to design controllers that are robust to sensor failures, and (ii) the computational expense of processing the high-dimensional sensor information in real time. This paper…

## 3 Citations

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A novel version of the generalized Fano inequality from information theory is used to demonstrate that a quantity that captures the amount of task-relevant information provided by a sensor provides an upper bound on the highest achievable expected reward for one-step decision making tasks.

### Differentially Private Timeseries Forecasts for Networked Control

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This work analyzes a scenario where the controller is able to pay the forecasting models an economic incentive, such as money, to lower their noise, and solves a biconvex optimization problem with guaranteed local optimality.

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Bounded rationality directly models the agent’s information processing ability, which is represented as the KL-divergence between nominal and optimized stochastic policies, and the solution to the bounded-optimal policy can be obtained by an e-cient importance sampling approach.

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