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
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R. Luo, R. Sinha, A. Hindy, S. Zhao, S. Savarese, E. Schmerling, and M. Pavone, “Online Distribution Shift Detection via Recency Prediction,” in Robotics: Science and Systems, 2023. (Submitted)
Abstract: When deploying modern machine learning-enabled robotic systems in high-stakes applications, detecting distributional shift is critical. However, most existing methods for detecting distribution shift are not well-suited to robotics settings, where data often arrives in a streaming fashion and may be very high-dimensional. In this work, we present an online method for detecting distributional shift with guarantees on the false positive rate — i.e., when there is no distribution shift, our system is very unlikely (with probability < ε) to falsely issue an alert; any alerts that are issued should therefore be heeded. Our method is specifically designed for efficient detection even with high dimensional data, and it empirically achieves up to 6x faster detection on realistic robotics settings compared to prior work while maintaining a low false negative rate in practice (whenever there is a distribution shift in our experiments, our method indeed emits an alert).
@inproceedings{LuoSinhaEtAl2023, author = {Luo, R. and Sinha, R. and Hindy, A. and Zhao, S. and Savarese, S. and Schmerling, E. and Pavone, M.}, booktitle = {{Robotics: Science and Systems}}, title = {Online Distribution Shift Detection via Recency Prediction}, year = {2023}, note = {Submitted}, url = {https://arxiv.org/abs/2211.09916}, keywords = {sub}, owner = {rdyro}, timestamp = {2022-09-21} } -
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.
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.
@inproceedings{SinhaHarrisonEtAl2022, 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 = {pub}, booktitle = {{American Control Conference}}, url = {https://arxiv.org/abs/2104.08261}, owner = {rhnsinha}, timestamp = {2022-01-31} } -
R. Sinha, J. Harrison, S. M. Richards, and M. Pavone, “Adaptive Robust Model Predictive Control via Uncertainty Cancellation,” IEEE Transactions on Automatic Control, 2022. (Submitted)
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 are commonly used to model the nonlinear effects of an unknown environment on a nominal linear system. Inspired by certainty equivalent “estimate-and-cancel” control laws pioneered in classical adaptive control, we optimize over a class of nonlinear feedback policies to significantly improve performance 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 model predictive control, 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 in the form of persistent constraint satisfaction with high probability. Finally, we show in simulation that our method can accommodate more significant unknown dynamics terms than existing methods.
@article{SinhaHarrisonEtAl2022b, author = {Sinha, R. and Harrison, J. and Richards, S. M. and Pavone, M.}, title = {Adaptive Robust Model Predictive Control via Uncertainty Cancellation}, journal = {{IEEE Transactions on Automatic Control}}, year = {2022}, note = {Submitted. Available at \url{https://arxiv.org/abs/2212.01371}}, keywords = {sub}, url = {https://arxiv.org/abs/2212.01371}, owner = {rhnsinha}, timestamp = {2023-01-30} }