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  • People

    • Charles Dawson
    • Kunal Garg
    • Kwesi Rutledge
    • Mingxin Yu
    • Oswin So
    • Ruixiao Yang
    • Rujul Gandhi
    • Songyuan Zhang
    • Yongchao Chen
    • Yue Meng
  • Our Research

    • Automated testing, verification & design optimization
    • Certified Learning for Control
    • Safe human-robot interaction and planning
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  • Publications
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  • People show submenu for “People”
    • Charles Dawson
    • Kunal Garg
    • Kwesi Rutledge
    • Mingxin Yu
    • Oswin So
    • Ruixiao Yang
    • Rujul Gandhi
    • Songyuan Zhang
    • Yongchao Chen
    • Yue Meng
  • Our Research show submenu for “Our Research”
    • Automated testing, verification & design optimization
    • Certified Learning for Control
    • Safe human-robot interaction and planning
  • News
  • Publications
  • Wiki
  • Contact
  • Prospective Members

Our Research

Our work lies at the intersection of control theory, machine learning, and formal methods. Here are some of the areas we're currently working on.
Home | REALM | Our Research

Research Themes

Certified learning for control

Before deploying deep learning-based methods for control, we need to understand the safety of these controllers. We develop tools to make learned controllers safer and generate data-driven proofs (certificates) of correctness.

Automated testing, verification & design optimization

As autonomous systems becomes more complex, they can start to fail in unexpected ways. We develop tools that help engineers discover, understand, and mitigate these failure modes.

Safe human-robot interaction and planning

We develop techniques for ensuring both the physical safety and intelligent responsiveness of robot agents in responding to complex human instructions, enabling robots to successfully work together with humans.
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