Shengxiang Sun
Papers
1
Total Citations
3
H-Index
1
About
Shengxiang Sun is a researcher at the forefront of robotics and artificial intelligence, specializing in the intersection of vision-language models and robotic skill acquisition. His work focuses on enabling robots to interpret complex human instructions and perform intricate manipulation tasks, particularly in furniture assembly—a domain requiring both high-level reasoning and precise motor control. Sun’s most notable contribution, the "Manual2Skill" framework (2025), demonstrates how robots can learn to read technical manuals and translate them into executable skills using vision-language models, bridging the gap between human-readable instructions and robotic action sequences. This pioneering approach has already garnered early attention, with 3 citations since its release, signaling its potential to reshape autonomous assembly in manufacturing and domestic settings. By integrating natural language understanding with robotic perception and planning, Sun addresses a critical challenge in making robots more adaptable and user-friendly. His work stands out for its practical application, tackling real-world tasks that require both cognitive and physical dexterity. As a rising voice in embodied AI, Sun’s research promises to accelerate the deployment of intelligent robots in everyday environments, making him a key figure to watch in the evolving landscape of robotic learning and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1