Papers
17
Total Citations
1,960
H-Index
13
About
Hongkai Dai is a prominent robotics researcher whose work sits at the intersection of motion planning, optimization, and control for legged and humanoid robots. Best known for his foundational contributions to the DARPA Robotics Challenge, his 2015 paper on locomotion planning for the Atlas humanoid robot has accumulated over 800 citations, establishing him as a leading voice in whole-body motion generation. Dai's research has consistently tackled one of robotics' hardest problems: how to make complex, high-degree-of-freedom robots move dynamically and robustly in the real world. His influential centroidal dynamics framework (2014, 418 citations) elegantly bridges the gap between oversimplified point-mass models and computationally intractable full-body dynamics, enabling practical whole-body motion planning. He has further advanced robust walking on uneven and unknown terrain through convex and semidefinite programming approaches, and extended his optimization expertise to grasp synthesis and wrench-based feasibility analysis. More recently, Dai has pursued the critical challenge of safety guarantees in learned controllers, exemplified by his work on Lyapunov-stable neural-network control. Across his career, his research has shaped how both academia and industry approach safe, reliable robot autonomy.
Research Focus
Key Achievements
Top Papers
- 1
- 2Whole-body motion planning with centroidal dynamics and full kinematics418 citations · 2014
- 3An Architecture for Online Affordance‐based Perception and Whole‐body Planning140 citations · 2014
- 4Planning robust walking motion on uneven terrain via convex optimization123 citations · 2016
- 5Lyapunov-stable neural-network control95 citations · 2021
- 6Optimizing robust limit cycles for legged locomotion on unknown terrain77 citations · 2012
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- 10Director: A User Interface Designed for Robot Operation with Shared Autonomy49 citations · 2016