Xiaohu Sang
Papers
1
Total Citations
16
H-Index
1
About
Xiaohu Sang is a robotics researcher whose work focuses on bridging the gap between machine learning and smooth, adaptive robot motion. His key research areas include deep reinforcement learning, dynamic movement primitives, and hierarchical control systems for robotic manipulation. Sang’s most notable contribution is the development of a hierarchical dynamic movement primitive framework, which integrates deep reinforcement learning to enable robots to execute fluid, obstacle-aware movements without jerky transitions. This work, published in 2022 and cited 16 times, addresses a critical challenge in real-world robotics: ensuring that learned behaviors are both efficient and physically smooth. By structuring movement into layered primitives, Sang’s approach allows robots to generalize from simulation to physical environments more reliably. His research has implications for industrial automation, assistive robotics, and human-robot interaction, where safe and natural motion is paramount. Sang’s work stands out for its practical focus on bridging high-level planning with low-level motor control, making him a rising voice in the field of learning-based robotics.
Research Focus
Key Achievements
Top Papers
- 1