Liang Tang
Papers
1
Total Citations
1
H-Index
1
About
Liang Tang is an emerging researcher specializing in robotics and artificial intelligence, with a particular focus on humanoid robot control, hierarchical policy learning, and dexterous manipulation. His work addresses one of the most formidable challenges in modern robotics: enabling humanoid systems to perform complex, coordinated whole-body tasks that seamlessly integrate locomotion, grasping, and fine manipulation within a unified framework. His most notable contribution, "Hierarchical Policy Learning for Humanoid Robots Whole-Body Dexterous Manipulation" (2025), tackles the high-dimensional complexity inherent in controlling humanoid robots equipped with dexterous hands — systems with extraordinary potential for applications in demanding environments such as space exploration and sampling missions. By developing hierarchical learning architectures, Tang's research provides a structured approach to decomposing and solving sequential manipulation challenges that have long resisted conventional methods. Though early in his citation trajectory with 1 citation to date, Tang's research addresses a frontier problem of significant importance to the robotics community. His work sits at the intersection of reinforcement learning, motion planning, and embodied AI, positioning him as a researcher to watch as the field of dexterous humanoid robotics rapidly advances.
Research Focus
Key Achievements
Top Papers
- 1