I am a research scientist at Horizon Robotics. Previously, I worked at Baidu Research and Facebook AI Research.

Over the course of my career, as the first author I have published papers in top AI conferences, covering a wide range of topics including deep RL, robotics, language grounding, embodied AI, multimodal learning, image/video segmentation, etc. I strongly believe that achieving AGI requires the integration of diverse disciplines. My endeavors have been recognized with distinction, particularly marked by the receipt of the Best Paper Award at ACL 2013 for my work on grounded language learning from video.

I am also an experienced engineer who loves writing code and building things. I am a co-author and an active maintainer of ALF, an RL framework emphasizing on the flexibility and easiness of implementing a complex RL system involving many different algorithm components.

My current passion is to combine all my past experience to build an agile robot, with mobile manipulation capabilities, that follows natural language commands in daily indoor scenarios.

ALF - Agent Learning Framework (ALF) is a PyTorch reinforcement learning framework emphasizing on the flexibility and easiness of implementing complex algorithms involving many different components.
TAAC - Temporally abstract actor-critic algorithm for efficient continuous control.
XWorld - A simulator package for RL research with language task support.
FLARE - A reinforcement learning framework for training embodied agents with PyTorch.

I like playing video games, especially ARPG. So far my favorite games are souls-like, by FromSoftware.