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Yutong Liu

Yutong Liu

Lecturer , Instructional Designer
Email

Contact

Lecturer
Cyber & Decision Sciences, School of
Email yutong.liu@csusb.edu
Office Phone(909) 537-4573
Office LocationPL-005
Instructional Designer
Multimedia & Immersive Technologies
Email yutong.liu@csusb.edu
Office Phone(909) 537-4573
Office LocationPL-005

Bio

Yutong Liu is an Adjunct Faculty member in the Information Systems and Technology (IST) Program at California State University, San Bernardino.

She specializes in Embodied AI, combining robotics, machine learning, computer vision, motion capture, digital humans, and immersive technologies to build intelligent systems that can perceive, communicate, and learn from the physical world.

At CSUSB, she serves as the Lead of the Motion Capture Studio, supporting interdisciplinary research and teaching in human movement analysis, virtual production, digital humans, and robotics.

Her experience includes:

  • Lead, CSUSB Motion Capture Studio
  • 5+ years of Unreal Engine 5 and digital human development
  • Embodied AI and humanoid robotics
  • Machine learning and computer vision
  • Motion capture and robot learning
  • AI-powered virtual humans and simulation

In her courses, students learn by building real AI applications—from digital humans to intelligent robots—using modern industry tools and research platforms. No robotics experience is required—just curiosity and a willingness to learn.

YouTube Channel 

LinkedIn

GitHub

 
 
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Integrating Large Language Models into Robotic Autonomy: A Review of Motion, Voice, and Training Pipelines

Liu, Y. (First Author), Sun, Q., & Kapadia, D. R. Enhancing Robotic Autonomy with Large Language Models: Locomotion, Voice Integration, and Training Frameworks. Published in AI (MDPI), 2025.
Enhancing Robotic Autonomy with Large Language Models presents a comprehensive survey of how large language models (LLMs) are transforming autonomous robotics. The paper examines applications in locomotion, navigation, manipulation, and voice interaction, while exploring training strategies, simulation-to-real-world transfer, and deployment architectures. It provides a practical framework and future directions for building intelligent, human-centered embodied AI systems.

 

Education

● Ph.D. Student in Data Science of Information Systems & Technology (CISAT), Claremont Graduate University, Claremont, CA (Fall 2025 till now)

● M.S., Computer Science, California State University, San Bernardino December 12,2024)
● Machine Learning Coursera of Stanford University (August 9, 2023)
● 6 certificates from Coursera of DeepLearning.AI
     ○ Unsupervised Learning, Recommenders, Reinforcement Learning (August 9,2023)
     ○ Supervised Machine Learning: Regression and Classification (May 29, 2023)
     ○ Advanced Learning Algorithms (July 2, 2023)
     ○ Linear Algebra for Machine Learning and Data Science (February 20, 2023)
     ○ Convolutional Neural Networks in TensorFlow (December 30, 2022)
     ○ Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning (April 17, 2022)

● M.A., Instructional Technology, California State University, San Bernardino (2019)
● B.A., English Literature, Xi’an International Studies, China (2003)

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