Joanne Truong

Research Abstract:

The goal of AI is to “construct useful intelligent systems”, such as mobile robots to assist in our day-to-day lives (e.g., robots delivering packages from one building to another). However, training robots in the real-world can be slow, dangerous, expensive, and difficult to reproduce. Thus, one paradigm in robot learning is to leverage simulation for training robots (where gathering experience is scalable, safe, cheap, and reproducible) before being deployed in the real world. My research leverages imperfect simulators for training robots for the real-world and has explored: (1) learning high-level and low-level robot skills using large scale learning (2) embodiment generalization, and (3) adaptation to novel environments such as the outdoors.

Bio:

Joanne Truong is a 5th year PhD student at Georgia Tech, co-advised by Prof. Sonia Chernova and Prof. Dhruv Batra. Her research lies at the intersection of machine learning and robotics. Her work focuses on pre-training AI agents for complex tasks in realistic simulators before transferring the skills learned to real robotic platforms. She is a recipient of the NSF Graduate Research Fellowship, NDSEG Fellowship (declined), Apple Scholars in AI/ML PhD Fellowship, Adobe Research Fellowship, and Google Women Techmakers Scholarship. She has interned at Meta AI, NVIDIA, Google Robotics, and Apple. Webpage: http://www.joannetruong.com/