Papers
8
Total Citations
105
H-Index
4
About
Weiqiao Han is a robotics and control researcher whose work spans reinforcement learning, motion planning, and feedback control for complex robotic systems. His research addresses some of the most challenging problems at the intersection of robot manipulation, legged locomotion, and stochastic systems under uncertainty. Han's early influential work on learning compound multi-step controllers (2015, 34 citations) tackled the practical challenge of deploying reinforcement learning agents within complex, sequential robotic tasks — moving beyond isolated episodic settings toward real-world applicability. His contributions to legged robot recovery through piecewise-affine quadratic regulators and hybrid systems modeling (2017, 17 citations) demonstrated rigorous control-theoretic approaches to multi-contact locomotion. His work on dexterous manipulation via piecewise affine approximations (2020, 14 citations) further established a principled model-based framework for handling non-smooth, nonlinear robotic systems. More recently, Han has made significant strides in risk-bounded trajectory optimization for stochastic nonlinear systems (2022, 30 citations), addressing non-Gaussian uncertainty — a critical and often overlooked challenge in real-world robot deployment. This growing body of work positions Han as an important contributor to safe, uncertainty-aware robotic planning and control, with cumulative citations reflecting meaningful influence across the robotics research community.
Research Focus
Key Achievements
Top Papers
- 1Learning compound multi-step controllers under unknown dynamics34 citations · 2015
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