Philip (Yizhou) Huang

I am a 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 (multiple) robots 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 and bimanual manipulation.

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!

Dataset and evaluation of an under-investigated problem in multi-robot arm motion planning.

An asynchronous planning and execution framework designed to safely and efficiently coordinate multiple robots arms to achieve cooperative assembly

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

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

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