Rohan Sinha


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Rohan Sinha

Rohan is a graduate student in the department of Aeronautics and Astronautics. Rohan’s research interests lie at the intersection of control theory, machine learning, and applied robotics. Currently, his research focuses on developing learning-based control algorithms with safety guarantees.

Previously, he received bachelor’s degrees in Mechanical Engineering and Computer Science from the University of California, Berkeley. As an undergraduate, Rohan worked on data-driven predictive control under Professor Francesco Borrelli in the Model Predictive Control Lab and on learning control algorithms that rely on vision systems under Professor Benjamin Recht in the Berkeley Artificial Intelligence Lab. He also interned as an autonomous driving engineer at Delphi (now Motional) and as a software engineer at Amazon.

In his free time, Rohan enjoys playing a variety of sports including sailing, tennis, soccer, and snowboarding.

ASL Publications

  1. R. Sinha, J. Harrison, S. M. Richards, and M. Pavone, “Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty,” in American Control Conference, 2022. (In Press)

    Abstract: We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally linear with an additive nonlinear component. Such systems commonly model the nonlinear effects of an unknown environment on a nominal system. We optimize over a class of nonlinear feedback policies inspired by certainty equivalent “estimate-and-cancel” control laws pioneered in classical adaptive control to achieve significant performance improvements in the presence of uncertainties of large magnitude, a setting in which existing learning-based predictive control algorithms often struggle to guarantee safety. In contrast to previous work in robust adaptive MPC, our approach allows us to take advantage of structure (i.e., the numerical predictions) in the a priori unknown dynamics learned online through function approximation. Our approach also extends typical nonlinear adaptive control methods to systems with state and input constraints even when we cannot directly cancel the additive uncertain function from the dynamics. Moreover, we apply contemporary statistical estimation techniques to certify the system’s safety through persistent constraint satisfaction with high probability. Finally, we show in simulation that our method can accommodate more significant unknown dynamics terms than existing methods.

      author = {Sinha, R. and Harrison, J. and Richards, S. M. and Pavone, M.},
      title = {Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty},
      year = {2022},
      keywords = {press},
      booktitle = {{American Control Conference}},
      url = {},
      owner = {rhnsinha},
      timestamp = {2022-01-31}