Yufeng Chi is a robotics-focused researcher and engineer with nine years of hands-on experience in building real-world robotic systems and cloud-enabled lab infrastructure. Based in Beijing and pursuing a PhD in EECS at UC Berkeley, he develops reinforcement learning controllers for the A1 robot dog using MuJoCo and OpenAI baselines and has built multi-node, real-time monitoring systems for lab automation. His background spans mechanical design for a full-size humanoid (cycloidal gear reducers and BLDC torque control) to embedded bring-up and ecosystem support roles at startups and Intel, giving him fluency across hardware, firmware, and control software. He also contributes to student engineering teams and produces technical content for a broad audience, signaling an ability to translate complex research into practical designs and accessible explanations.
8 years of coding experience
1 year of employment as a software developer
Bachelor of Science - BS, EECS / Electrical Engineering and Computer Science, 3.714, Bachelor of Science - BS, EECS / Electrical Engineering and Computer Science, 3.714 at University of California, Berkeley
High School, International Curriculum Center, High School, International Curriculum Center at The High School Affiliated to Renmin University of China
Non Degree Program Pre-bac Career, Engineering & Applied Science Undergrad, Management & Technology Summer Institute, A-, Non Degree Program Pre-bac Career, Engineering & Applied Science Undergrad, Management & Technology Summer Institute, A- at University of Pennsylvania
Contributions:15 PRs, 31 pushes, 9 branches in 2 years 4 months
motor-controllerrecoil-control-system
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