Headshot of Kyle Hsu

Kyle Hsu

徐宏愷

PhD Candidate, Stanford University

kylehsu@cs.stanford.edu

I'm a final year computer science PhD student advised by Chelsea Finn and Jiajun Wu. Much of my PhD has been generously supported by a Sequoia Capital Stanford Graduate Fellowship in Science & Engineering and an NSERC Postgraduate Scholarship – Doctoral. I'm a bilingual Taiwanese-Canadian.

My recent research comprises two themes: (i) tooling and inductive biases for robot learning in the age of foundation models, and (ii) disentangled representation learning of high-dimensional data for interpretability and safety. I'm seeking full-time AI research and engineering roles related to these topics with a start date in Q2–Q3 2025.

Previously, I studied engineering science at the University of Toronto. This summer, I'm working on foundation models for robotics at Toyota Research Institute. In my free time, I like to ski & snowboard and play Soulslike & board games.

selected publications

robot learning

Evaluating real-world robot manipulation policies in simulation
Xuanlin Li*, Kyle Hsu*, Jiayuan Gu*, Karl Pertsch, Oier Mees, Homer Rich Walke, Chuyuan Fu, Ishikaa Lunawat, Isabel Sieh, Sean Kirmani, Sergey Levine, Jiajun Wu, Chelsea Finn, Hao Su, Quan Vuong, Ted Xiao
under review
DGR@RSS2024 spotlight
paper project page
Vision-based manipulators need to also see from their hands
Kyle Hsu*, Moo Jin Kim*, Rafael Rafailov, Jiajun Wu, Chelsea Finn
ICLR 2022 oral
paper project page

disentangled representation learning

Tripod: three complementary inductive biases for disentangled representation learning
Kyle Hsu*, Jubayer Ibn Hamid*, Kaylee Burns, Chelsea Finn, Jiajun Wu
ICML 2024
paper poster code
Disentanglement via latent quantization
Kyle Hsu, Will Dorrell, James CR Whittington, Jiajun Wu, Chelsea Finn
NeurIPS 2023
paper poster code

unsupervised meta-learning

Unsupervised curricula for visual meta-reinforcement learning
Allan Jabri, Kyle Hsu, Ben Eysenbach, Abhishek Gupta, Sergey Levine, Chelsea Finn
NeurIPS 2019 spotlight
paper project page
Unsupervised learning via meta-learning
Kyle Hsu, Sergey Levine, Chelsea Finn
ICLR 2019
paper project page

scalable abstraction-based controller synthesis

Lazy abstraction-based controller synthesis
Kyle Hsu, Rupak Majumdar, Kaushik Mallik, Anne-Kathrin Schmuck
ATVA 2019 invited paper
paper code
Multi-layered abstraction-based controller synthesis for continuous-time systems
Kyle Hsu, Rupak Majumdar, Kaushik Mallik, Anne-Kathrin Schmuck
HSCC 2018
paper code