Overview
Boyuan Chen is a professor at Duke University where he directs General Robotics Lab. His research is driven by the mission of building Discovery Machines that can learn, act, and collaborate by continually discovering how the world works on its own. The following fundamental questions guide his work:
- Can we build truly cognitive machines?
- What does it take for machines to learn, evolve, and adapt on their own, continuously improving through experience and interaction?
- How can we enable machines to be true partners with humans and augment human intelligence?
- What does embodiment mean and what should it look like?
- Can science itself be automated and accelerated to go beyond human intuition?
Answers to these questions can help illuminate the mysteries of intelligence, both artificial and natural, and pave the way for machines that not only perform tasks but truly understand, learn, and connect.
Boyuan Chen obtained his Ph.D. in Computer Science at Columbia University with Hod Lipson.
Current Appointments & Affiliations
Dickinson Family Assistant Professor of Mechanical Engineering & Materials Science
·
2025 - Present
Thomas Lord Department of Mechanical Engineering and Materials Science,
Pratt School of Engineering
Assistant Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science
·
2022 - Present
Thomas Lord Department of Mechanical Engineering and Materials Science,
Pratt School of Engineering
Assistant Professor in the Department of Electrical and Computer Engineering
·
2022 - Present
Pierre R. Lamond Department of Electrical and Computer Engineering,
Pratt School of Engineering
Assistant Professor in Computer Science
·
2023 - Present
Computer Science,
Trinity College of Arts & Sciences
Recent Publications
Extreme dynamic symmetry enables omnidirectional and multifunctional robots.
Journal Article Science robotics · May 2026 Symmetry is a central organizing principle in natural systems, yet its use as a unifying design strategy in robotics has largely remained limited to geometric form. We show that symmetry can instead be leveraged at the level of dynamic actuation capability ... Full text CiteLearning realistic lip motions for humanoid face robots.
Journal Article Science robotics · January 2026 Lip motion represents outsized importance in human communication, capturing nearly half of our visual attention during conversation. Yet anthropomorphic robots often fail to achieve lip-audio synchronization, resulting in clumsy and lifeless lip behaviors. ... Full text CitePhysical artificial intelligence in nursing: Robotics.
Journal Article Nursing outlook · September 2025 BackgroundRobotics, driven by advancements in physical artificial intelligence (AI), offers potential solutions-yet many challenges- to creating innovative care models to meet the needs of the future.PurposeTo present an overview of robot ... Full text CiteRecent Grants
Scalable and Adaptive Multi-Agent Framework for Human-AI Teaming Across Hierarchies and Time Horizons
ResearchPrincipal Investigator · Awarded by Army Research Laboratory · 2024 - 2028Accelerating Scientific Discovery via Automatic Discovered State Variables
ResearchPrincipal Investigator · Awarded by Army Research Laboratory · 2024 - 2027Population-Based Training for Multi-Human Multi-Machine Teaming with Enhanced Performance via Neurostimulation
ResearchPrincipal Investigator · Awarded by Army Research Laboratory · 2023 - 2027View All Grants
Education
Columbia University ·
2022
Ph.D.