Papers
3
Total Citations
35
H-Index
3
About
Qi Huo is a robotics researcher focused on enabling safe and adaptive physical interaction between robots and their environments. His core research areas include impedance control, learning from demonstration, and reinforcement learning for manipulation. Huo’s major contributions center on developing control frameworks that allow robots to handle uncertain or uncalibrated contact conditions. His most cited work, “Impedance estimation for robot contact with uncalibrated environments” (2021, 16 citations), proposes methods for robots to autonomously estimate environmental stiffness and damping during contact, eliminating the need for precise pre-calibration. In a complementary study, “Reinforcement Learning with Dynamic Movement Primitives for Obstacle Avoidance” (2021, 15 citations), Huo integrates reinforcement learning with dynamic movement primitives (DMPs) to generate robust, obstacle-avoiding trajectories while preserving the stability guarantees of DMPs. His work on “Virtual semi-active damping learning control” (2020) further advances adaptive control for manipulators interacting with unknown environments. Collectively, Huo’s research bridges model-based control and data-driven learning, offering practical solutions for industrial and service robots that must operate reliably in unpredictable settings.
Research Focus
Key Achievements
Top Papers
- 1Impedance estimation for robot contact with uncalibrated environments16 citations · 2021
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