I am broadly interested in reinforcement learning, robotics, game playing, deep learning, and artificial intelligence.
In particular, I'm interested in sample-efficient learning in vision-based settings for simulated and real robotic systems.
ReLMM: Practical RL for Learning Mobile Manipulation Skills using Only Onboard Sensors
Under review as a conference paper at RSS 2021
We study how mobile manipulators can autonomously learn skills that require a combination of navigation
and grasping. We propose ReLMM, a mobile manipulation system that can learn continuously on a real-world
platform without any environment instrumentation, with minimal human intervention, and without access to
privileged information, such as maps, objects positions, or a global view of the environment.
As a Teaching Assistant, I lead weekly discussion sections and hold office hours to help students.
EECS 126: Probability and Random Processes
Teaching Assistant: Spring 2021
CS 170: Efficient Algorithms and Intractable Problems
Teaching Assistant: Fall 2020
CS 61A: Structure and Interpretation of Computer Programs
Teaching Assistant: Spring 2020, Fall 2019