Welcome
Hello! I am a Master’s student at Stanford University. I completed my B.S. with Honors in Symbolic Systems and a minor in Mathematics, and am currently pursuing a coterminal M.S. in Computer Science. I conduct research with Stanford’s IPRL Lab, affiliated with the Stanford Artificial Intelligence Laboratory (SAIL), which seeks to understand the underlying principles of robust sensorimotor coordination by implementing them on robots. I am fortunate to be mentored by Jeannette Bohg, and previously by Chelsea Finn, Suraj Nair, and Annie Xie of Stanford’s IRIS Lab.
My research interests span robotics, machine learning, and computer vision. I’m interested in enabling robots to learn generalizable representations from diverse datasets, and refine them through interaction for performing complex tasks in the real world. I’m especially interested in:
- Representation learning from diverse data: Developing robust state and action representations for robot learning by extracting useful interaction priors from diverse human and robot data.
- Learning through interaction: Enhancing precision and robustness of pre-trained policies via training in high-fidelity simulation and sample-efficient online fine-tuning.
- Human-robot collaboration: Enabling robots to continually practice skills and acquire in-domain data autonomously, while interactively adapting to intermittent human feedback or interventions.
- Robust manipulation and control: Integrating adaptive control and perception systems with robot learning to robustly respond to dynamic changes and occlusions.
If any of the above sounds interesting to you, I would love to hear from you! Feel free to reach me at oliviayl [at] stanford [dot] edu.