Tsung-Wei Ke
Papers
1
Total Citations
4
H-Index
1
About
Tsung-Wei Ke is a rising researcher in robot learning, with a focus on advancing policy representations for complex manipulation tasks. His key research areas include diffusion-based policy learning, 3D scene understanding, and visuomotor control for robotics. Ke’s most notable contribution is the development of “3D Diffuser Actor,” a novel framework that integrates diffusion policies with 3D scene representations. This work addresses a critical limitation in prior methods by enabling robots to learn action distributions conditioned on rich, spatially grounded 3D features, rather than flat 2D images. The approach has already garnered early citations, signaling its potential to influence how robots perceive and interact with their environments. By bridging the gap between diffusion models and 3D reasoning, Ke is helping to push the boundaries of dexterous, real-world robot performance. His work is particularly relevant for researchers interested in scalable imitation learning and embodied AI. As his publication record grows, Tsung-Wei Ke is establishing himself as a thoughtful contributor to the next generation of robot learning systems.
Research Focus
Key Achievements
Top Papers
- 13D Diffuser Actor: Policy Diffusion with 3D Scene Representations4 citations · 2024