IEEE Robotics and Automation Letters - 2021

we propose the use of single-chip millimeter-wave (mmWave) radar, which is lightweight and inexpensive, for learning-based autonomous navigation. However, because mmWave radar signals are often noisy and sparse, we propose a cross-modal contrastive learning for representation (CM-CLR) method that maximizes the agreement between mmWave radar data and LiDAR data in the training stage to enable autonomous navigation using radar signal.

Urban Circuit - Seatle - 2020

Journal of Field Robotics - 2022

The DARPA Subterranean (SubT) Challenge aims to develop innovative technologies that would augment operations underground. The SubT Challenge will explore new approaches to rapidly map, navigate, search, and exploit complex underground environments. I participated in the Urban Circuit as the software team lead and human supervisor. We build our robots to perform search and rescure mission in a unfinished nuclear power plant.

Frontier in Robotics and AI - 2021

Facilitating navigation in pedestrian environments is critical for enabling people who are blind and visually impaired (BVI) to achieve independent mobility. A deep-reinforcement-learning-based assistive guiding robot with ultrawide-bandwidth (UWB) beacons that can navigate through routes with designated waypoints was designed in this study.

We present our socially-aware end-to-end grasping for human-to-robot handover. We first leverage existing end-to-end grasping as network backbone, and then finetune for non-invasive grasps and trajectories using sample efficient deep reinforcement learning. Comprehensive evaluations are carried out against various recent baselines using multi-stage hand and object prediction and subsequent planning.

Tunnel Circuit - Pittsburgh - 2019

The DARPA Subterranean (SubT) Challenge aims to develop innovative technologies that would augment operations underground. The SubT Challenge will explore new approaches to rapidly map, navigate, search, and exploit complex underground environments. I participated in the Tunnel Circuit with my team and our robots to perform search and rescue mission in a mine tunnel.

IEEE/RSJ International Conference on Intelligent Robots and Systems - 2019

Sixth Biennial Meeting of MOOS Development and Application Working Group, Cambridge MA, August, 2019

This project is to help the people who are interested in marine robotics. We built a low-cost surface vehicle called duckieboat. Our vehicles are compatible with machine learning frame works. We demonstrate the implementation of classic autonomous navigation algorithms also tracking vehicles with deep learning computer vision with sensors on board.

Hawaii- 2018

We participated in the 2018 Maritime RobotX Challenge and awarded number 5th at the final stage. We built a surface vehicle to solve multiple tasks including autonomous docking, navigation, obstacle avoidance, launch & recovery, scan code and acoustic pinging.