Rohan Sinha
PhD Student
Rohan is a PhD candidate in the department of Aeronautics and Astronautics. His research focuses on developing methodologies that improve the reliability of ML-enabled robotic systems, particularly when these systems encounter out-of-distribution conditions with respect to their training data. Broadly, his research interests lie at the intersection of control theory, machine learning, and applied robotics.
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 Prof. Francesco Borrelli in the Model Predictive Control Lab and on learning control algorithms that rely on vision systems under Prof. Benjamin Recht in the Berkeley Artificial Intelligence Lab. He has 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.
Publications
13 publications · full lab bibliography
- M. Ganai, R. Sinha, C. Agia, D. Morton, L. Di Lillo, and M. Pavone, “Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning,” Conf. on Robot Learning, 2025. (In Press)
- J. Kwok, C. Agia, R. Sinha, M. Foutter, S. Li, I. Stoica, A. Mirhoseini, and M. Pavone, “RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models,” Conf. on Robot Learning, 2025. (In Press)
- C. Agia, R. Sinha, J. Yang, R. Antonova, M. Pavone, H. Nishimura, M. Itkina, and J. Bohg, “CUPID: Curating Data your Robot Loves with Influence Functions,” Conf. on Robot Learning, 2025. (In Press)
- F. Marchiori, R. Sinha, C. Agia, A. Robey, G. J. Pappas, M. Conti, and M. Pavone, “Preventing Robotic Jailbreaking via Multimodal Domain Adaptation,” Proc. IEEE Conf. on Robotics and Automation, 2025. (Submitted)
- C. Agia, R. Sinha, J. Yang, Z. Cao, R. Antonova, M. Pavone, and J. Bohg, “Unpacking Failure Modes of Generative Policies: Runtime Monitoring of Consistency and Progress,” Conf. on Robot Learning, 2024. (In Press)
- R. Sinha, A. Elhafsi, C. Agia, M. Foutter, E. Schmerling, and M. Pavone, “Real-Time Anomaly Detection and Planning with Large Language Models,” Robotics: Science and Systems, 2024.
- R. Luo, R. Sinha, Y. Sun, A. Hindy, S. Zhao, S. Savarese, E. Schmerling, and M. Pavone, “Online Distribution Shift Detection via Recency Prediction,” Proc. IEEE Conf. on Robotics and Automation, 2024.
- M. Foutter, R. Sinha, S. Banerjee, and M. Pavone, “Self-Supervised Model Generalization using Out-of-Distribution Detection,” Conf. on Robot Learning - Workshop on Out-of-Distribution Generalization in Robotics, 2023.
- A. Elhafsi, R. Sinha, C. Agia, E. Schmerling, I. A. D. Nesnas, and M. Pavone, “Semantic Anomaly Detection with Large Language Models,” Autonomous Robots, vol. 47, no. 8, pp. 1035--1055, 2023.
- R. Sinha, E. Schmerling, and M. Pavone, “Closing the Loop on Runtime Monitors with Fallback-Safe MPC,” Proc. IEEE Conf. on Decision and Control, 2023.
- R. Sinha, S. Sharma, S. Banerjee, T. Lew, R. Luo, S. M. Richards, Y. Sun, E. Schmerling, and M. Pavone, “A System-Level View on Out-of-Distribution Data in Robotics,” 2022.
- R. Sinha, J. Harrison, S. M. Richards, and M. Pavone, “Adaptive Robust Model Predictive Control via Uncertainty Cancellation,” IEEE Transactions on Automatic Control, 2022. (In Press)
- R. Sinha, J. Harrison, S. M. Richards, and M. Pavone, “Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty,” American Control Conference, 2022.