About Me
I'm Yuzhou Chen (陈禹舟), a dual-degree Master’s student in Electrical & Computer Engineering and Mechanical Engineering at the University of Michigan. I specialize in robotics, machine learning, and perception-driven control systems. I’ve led research in robot learning, robot manipulation, and motion planning using transformer-based architectures and RL.
Education
University of Michigan, Ann Arbor
M.S. in Electrical and Computer Engineering• Focused on Machine Learning, GPA: 3.73/4.0
M.S.E. in Mechanical Engineering
• Focused on Robotics and Mechatronics, GPA: 3.73/4.0
Aug 2022 – May 2025
Jilin University, Changchun, China
B.E. in Mechanical Engineering• Focused on Robotics and Mechatronics, GPA: 87.1/100
Aug 2018 – Jun 2022
Skills
Programming Languages
Python, C++, HTML/CSS, C, SQL, MATLAB, JavaScript, Arduino
MLOps and Software Tools
Deep Learning: PyTorch, GPyTorch, TensorFlow
Data Science: NumPy, Pandas, OpenCV, matplotlib, scikit-learn
Robotics/Simulation: ROS, IsaacSim
RL Libraries: Gym, Stable-Baselines3
Cloud & DevOps: Docker, AWS EC2/S3, Git
Machine Learning and Optimization
NLP: LLM, Transformer, BERT, GPT
Generative Models: GANs, VAE, Diffusion Models
Probabilistic Models: GMM, GP
RL: DDPG, PPO, Diffusion
Computer Vision SoTA: SAM, DUST3R, Mask3D
Engineering & Simulation Tools
ANSYS / Workbench, Abaqus, SolidWorks, UG NX, CATIA, AutoCAD, Mathematica, LaTeX
Projects
Bayesian Optimization for Learning-Based Multi-Body Manipulation |
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Learned multi-body dynamics where a robot uses an intermediate object to push a target object to a goal, and applied Model Predictive Path Integral (MPPI) control enhanced with Bayesian Optimization for efficient trajectory planning. |
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Obstacle free
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Obstacle awearness
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Learning-Based Robot Planning with PPO and Diffusion Policy |
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Implemented and compared Proximal Policy Optimization (PPO) and Diffusion Policy for robot motion planning and control, focusing on continuous action spaces in manipulation tasks. |
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PPO
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Diffusion Policy
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3D Semantic Perception for Robotics in Simulated Aircraft Cabins |
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Developed a simulation pipeline in IsaacSim to enable robotic perception in cluttered cabin environments. |
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3D Reconstruction
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Environment in IsaacSim
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Vision-Based Robot Control in Latent Space |
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Encoded raw image observations into a latent space using a Variational Autoencoder (VAE), then applied Model Predictive Path Integral (MPPI) control in the latent space. |
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VAE Latent Control Demo
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State Image Input
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Gaussian Process Based Robot Pushing with Obstacle Avoidance |
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Implemented Gaussian Process (GP) to capture uncertain system dynamics, and applied Model Predictive Path Integral (MPPI) to enable obstacle-aware object pushing under uncertainty. |
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GP-Based Pushing Demo
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Prediction with GP
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BotLab Autonomous Mobile Robot |
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Differential-drive robot with 2D LiDAR for autonomous block transport and navigation, integrating SLAM and A-star planning. |
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Auto-navigation
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SLAM
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ArmLab 5-DOF Robotics Suite |
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Building autonomy for a 5-DOF robotic arm using computer vision, forward and inverse kinematics, and path planning to manipulate various objects. |
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Auto-stacking
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Environment
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Sequential Manipulation in PyBullet |
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Reasoning with pushing and grasping actions to build towers from objects in simulation using programmable primitives. |
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Autonomous Racing & Obstacle Avoidance |
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Control strategies for high-speed racing with embedded obstacle avoidance mechanisms. |
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