Chia-Man Hung
Papers
1
Total Citations
14
H-Index
1
About
Chia-Man Hung is a roboticist whose research lies at the intersection of machine learning, generative modeling, and manipulation planning. Their most-cited work, "Reaching Through Latent Space: From Joint Statistics to Path Planning in Manipulation" (2022, 14 citations), introduces a novel approach to path planning for robotic manipulators. Instead of searching directly in high-dimensional configuration spaces, Hung leverages the latent space of a generative model of robot poses, enabling iterative optimization for efficient path generation. A key innovation is the integration of constraint satisfaction classifiers within this latent space, allowing physical and task-specific constraints to be seamlessly incorporated into the planning process. This work bridges statistical learning and robotics, offering a more flexible and data-driven alternative to traditional sampling-based planners. Though early in their career, Hung’s contributions demonstrate a clear vision for how generative models can enhance robotic autonomy. Their research is particularly relevant for complex manipulation tasks where conventional planning struggles. As the field moves toward tighter integration of perception and control, Hung’s latent-space methodology stands out as a promising direction for scalable, constraint-aware robot motion.
Research Focus
Key Achievements
Top Papers
- 1