Philip (Yizhou) Huang

I am a first-year Ph.D. student in the Robotics Institute at Carnegie Mellon University, supervised by Prof. Jiaoyang Li . I completed my MSc in Computer Science at the University of Toronto, co-advised by Prof. Florian Shkurti and Prof. Tim Barfoot. I received a Bachelor of Applied Science in Engineering Science from the University of Toronto.

I have been fortunate to work under many fantastic researchers in robotics. Previously, I collabrated with Prof. Fabio Ramos from Nvidia on task and motion planning. In undergrad, I was involved in the student design team aUToronto, where we competed and won the SAE Autodrive Challenge. I also spent one amazing summer in Israel with Prof. Amir Degani and the summer before that with Prof. Angela Schoellig.

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My research focuses on developing planning algorithms for robot and robot teams to accomplish long-horizon tasks in dynamic and uncertain environment. I am particuarly interested in solving problems in real-world robotics and have worked on many different applications, such as multi-robot assembly, autonomous surface vessels (ASV) for environmental monitoring, and self-driving cars. I have also worked on continual learning for robot dynamics modeling and control.

If you have any questions / want to collaborate, feel free to reach out and send me an email! I am very excited to talk with more people and learn about your work!

Field testing and system descriptions of our GPS-, vision-, and sonar-enabled autonomous vessel for water-quality monitoring in freshwater lakes.

Leveraging parallel and differentiable simulation to efficiently search for multiple diverse plans with gradient-based variational inference.

A robust route-planning algorithm uses satellite images as a corase map to plan water sampling routes for autonomous surface vessels (ASV) given environmental disturbances.

Task-conditioned hypernetworks can be used to continually adapt to varying environment dynamics in lifelong model-based reinforcement learning, with a fixed-size replay buffer.

System design and development of the winning self-driving car in the AutoDrive Challenge, as well as lessons learned.

In continual learning, self-tuning network (STN) is a memory-efficient and easy-to-implement hypernetworks architecture with strong performance on many benchmarks.

RL can be applied to the Minimum Latency Problem by using a graph attention network to encode stochastic policy for constructively building partial paths, yielding solutions which are comparable to state-of-the-art, hand-engineered methods.

Re-implemented the SIGGRAPH 96 paper from David Baraff that introduced a linear-time sparse solver with Lagrangian multiplers in MATLAB. Verified that the time complexity of our re-implemented sparse solver is linear with serial chains and trees.

We measured the quantitative performance of recent language models for text style transfer, using three metrics and third-party models for fair comparison.

Re-implementation of the paper PIXOR: Real-time 3D object Detection from Point Clouds using PyTorch.

Indoor localization and navigation of a Crazyflie nano-quadcopter with a RGB-D camera for pollinating sunflowers.

Designing software framework and simulations for flying a swarm of 9 Crazyflie nano-quadcopters indoors.

This template is from Jon Barron's website. Here is the source code