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

@article{nishimura_rat_2021,
  title = {{RAT} {iLQR}: {A} {Risk} {Auto}-{Tuning} {Controller} to {Optimally} {Account} for {Stochastic} {Model} {Mismatch}},
  volume = {6},
  issn = {2377-3766, 2377-3774},
  shorttitle = {{RAT} {iLQR}},
  url = {https://ieeexplore.ieee.org/document/9312440/},
  abstract = {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.},
  language = {en},
  number = {2},
  urldate = {2021-04-09},
  journal = {IEEE Robotics and Automation Letters},
  author = {Nishimura, Haruki and Mehr, Negar and Gaidon, Adrien and Schwager, Mac},
  month = apr,
  year = {2021},
  keywords = {Collision Avoidance, Collision avoidance, Cost function, Optimal control, Optimization and Optimal Control, Probability distribution, Robot sensing systems, Robust control, Robust/Adaptive Control, Stochastic processes},
  pages = {763--770},
  month_numeric = {4}
}