Bo-Ruei (Ray) Huang

I am a first-year Ph.D. student in Computer Science at the University of Southern California, advised by Erdem Bıyık in the LiraLab.

I received my Bachelor's degrees in Electrical Engineering and Computer Science from National Taiwan University, where I was advised by Shao-Hua Sun in the NTU Robot Learning Lab. In 2024, I visited MIT CSAIL, working with Jiayuan Mao, Xiaolin Fang, Leslie Pack Kaelbling, and Joshua B. Tenenbaum. Previously, in the summer of 2023, I worked with Yuk L. Yung at Caltech GPS.

Email (boruei.huang [AT] usc.edu)  /  CV  /  Google Scholar  /  Twitter  /  GitHub  /  LinkedIn

News

Research

The goal of my research is to make robots learn to help humans in their daily lives, especially in long-horizon and open-world tasks. I am interested in reinforcement learning, imitation learning, planning, and representation learning.

Pre-Prints

DEXOP is a hand exoskeleton system designed to teach robots dexterous manipulation in-the-wild with tactile sensing and haptic feedback.

Publications

KALM uses VLMs to automatically generate task-relevant keypoints for better generalization in robot manipulation tasks.

DIFO is a learning from demonstration algorithm that integrates diffusion models to model state transitions and provide robust rewards to improve policy learning without action labels.

An improved machine learning algorithm refines aerosol data from OCO satellite spectra to enhance CO2 retrieval accuracy and advance climate impact models of volcanic aerosols.

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