Yixiao (Alvin) Sun
Alvin is a graduate student in the Mechanical Engineering department. He is currently focusing on uncertainty calibration for machine learning models and exploring how those uncertainties can be utilized in a robot autonomy stack. In general, Alvin has a broad interest that spans robotics, deep learning, computer vision, optimization, and embedded systems.
Prior to joining Stanford, Alvin graduated with highest honors from UIUC with a B.S. in Computer Engineering. At UIUC, Alvin worked in the Intelligent Motion Lab where he built real-time 3D semantic reconstruction algorithms for autonomous UV disinfection robots. Alvin has also held internships with Megvii and Skydio where he developed skills in deep visual tracking.
Outside of research, Alvin enjoys playing the piano, volleyball, skiing, and rollerblading.
Awards:
- Stanford Graduate Fellowship (2021)
- UIUC Bachelor of Science in Computer Engineering with Highest Honors (2021)
- UIUC Bronze Tablet Award (2021)
ASL Publications
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R. Luo, R. Sinha, Y. Sun, A. Hindy, S. Zhao, S. Savarese, E. Schmerling, and M. Pavone, “Online Distribution Shift Detection via Recency Prediction,” in Proc. IEEE Conf. on Robotics and Automation, 2024. (In Press)
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 Sun, Y. and Hindy, A. and Zhao, S. and Savarese, S. and Schmerling, E. and Pavone, M.}, booktitle = {{Proc. IEEE Conf. on Robotics and Automation}}, title = {Online Distribution Shift Detection via Recency Prediction}, year = {2024}, keywords = {press}, note = {In press}, url = {https://arxiv.org/abs/2211.09916}, owner = {rdyro}, timestamp = {2022-09-21} } -
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.
Abstract: When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of black-box learned components in the modern robot autonomy stack. Therefore, coping with OOD data is an important challenge on the path towards trustworthy learning-enabled open-world autonomy. In this paper, we aim to demystify the topic of OOD data and its associated challenges in the context of data-driven robotic systems, drawing connections to emerging paradigms in the ML community that study the effect of OOD data on learned models in isolation. We argue that as roboticists, we should reason about the overall system-level competence of a robot as it performs tasks in OOD conditions. We highlight key research questions around this system-level view of OOD problems to guide future research toward safe and reliable learning-enabled autonomy.
@inproceedings{SinhaSharmaEtAl2022, author = {Sinha, R. and Sharma, S. and Banerjee, S. and Lew, T. and Luo, R. and Richards, S. M. and Sun, Y. and Schmerling, E. and Pavone, M.}, title = {A System-Level View on Out-of-Distribution Data in Robotics}, year = {2022}, keywords = {}, url = {https://arxiv.org/abs/2212.14020}, owner = {rhnsinha}, timestamp = {2022-12-30} }