Papers
11
Total Citations
200
H-Index
7
About
Richard Cheng is a robotics researcher whose work spans robot safety, motion planning, aerial robotics, and mobile manipulation. He is perhaps best known for his contributions to safe multi-agent interaction, particularly through the development of robust Control Barrier Functions (CBFs) that incorporate learned uncertainty models — work that has garnered over 70 citations and represents a significant advance in making theoretical safety guarantees applicable to real-world, unpredictable environments. His early research tackled the formidable challenge of wind disturbance rejection for millimeter-scale flapping-wing robots, demonstrating a rare breadth that bridges microscale bio-inspired systems and large-scale autonomous platforms. Cheng has also made meaningful contributions to sampling-based motion planning, exploring how learned sampling distributions can improve efficiency in high-dimensional configuration spaces, and more recently developed GPU-accelerated convex set construction for real-time planning. His 2023 work on mobile manipulation in an unmodified grocery store exemplifies his commitment to deploying robust robotic systems beyond controlled laboratory settings. Collectively, his research addresses one of the field's most pressing challenges: building robots that are not only capable but provably safe when operating alongside humans in complex, dynamic environments.
Research Focus
Key Achievements
Top Papers
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- 3Learning an Optimal Sampling Distribution for Efficient Motion Planning19 citations · 2020
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- 5Demonstrating Mobile Manipulation in the Wild: A Metrics-Driven Approach13 citations · 2023
- 6Wind disturbance rejection for an insect-scale flapping-wing robot9 citations · 2015
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