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

arXiv: https://arxiv.org/abs/2010.08174 · HTML: https://mlsanity.com/paper/2010.08174/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity](https://mlsanity.com/paper/2101.01041/) (2021)
- [Learning Latent Representations to Co-Adapt to Humans](https://mlsanity.com/paper/2212.09586/) (2022)
- [Adaptive Teaching in Heterogeneous Agents: Balancing Surprise in Sparse Reward Scenarios](https://mlsanity.com/paper/2405.14199/) (2024)
- [Unifying Entropy Regularization in Optimal Control: From and Back to Classical Objectives via Iterated Soft Policies and Path Integral Solutions](https://mlsanity.com/paper/2512.06109/) (2025)

## Abstract (arXiv)

Successful robotic operation in stochastic environments relies on accurate characterization of the underlying probability distributions, yet this is often imperfect due to limited knowledge. This work presents a control algorithm that is capable of handling such distributional mismatches. Specifically, we propose a novel nonlinear MPC for distributionally robust control, which plans locally optimal feedback policies against a worst-case distribution within a given KL divergence bound from a Gaussian distribution. Leveraging mathematical equivalence between distributionally robust control and risk-sensitive optimal control, our framework also provides an algorithm to dynamically adjust the risk-sensitivity level online for risk-sensitive control. The benefits of the distributional robustness as well as the automatic risk-sensitivity adjustment are demonstrated in a dynamic collision avoidance scenario where the predictive distribution of human motion is erroneous.