Multimodal AI and Robotic Systems (MARS) Lab

Assistant Professor Yang Jianfei

Research Vision
  • MARS Lab studies Physical AI, focusing on how artificial intelligence can empower physical systems—such as robotics and IoT—to perceive, understand, and interact with the world through multimodal learning.
  • Our vision is to create intelligent robots and systems that seamlessly integrate into human society, enhancing productivity and empowering people to lead more fulfilling and prosperous lives.
Core Research Areas
  • Multimodal AI (e.g., VLM, M-LLM)
  • Embodied AI (e.g., Robot Learning and VLA)
  • Efficient AI (e.g., Transfer Learning and TinyML)
Applications & Impact
  • Multimodal LLM for human-robot interaction
  • AIoT sensing for healthcare and elderly care
  • AI-powered robotic arms for lab automation and factory
  • Edge intelligence for smart home and building

 

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