Jingwen Cheng
Papers
1
Total Citations
12
H-Index
1
About
Jingwen Cheng is a rising force in robotics, whose work pushes the boundaries of distributed manipulation and tactile intelligence. Her research centers on developing systems that can perceive, support, and interact with objects across an entire surface, moving beyond traditional single-arm grippers. Her landmark paper, "ArrayBot: Reinforcement Learning for Generalizable Distributed Manipulation through Touch" (2024, 12 citations), introduces a revolutionary platform: a 16×16 array of vertically sliding pillars, each integrated with tactile sensors. This design allows for simultaneous, coordinated manipulation of tabletop objects, enabling tasks like reorienting, sorting, and deforming items without prior knowledge of their shape or material. By combining this novel hardware with reinforcement learning, Cheng demonstrates how a dense "touch skin" can enable robots to generalize across diverse objects, a critical step toward more adaptable and safe automation. Her work has quickly garnered attention for its elegant fusion of sensing and control, earning her recognition as a pioneer in tactile-based distributed manipulation. For students and researchers, Cheng’s research offers a compelling vision of how robots might one day handle the messy, unpredictable world with the dexterity of human hands.
Research Focus
Key Achievements
Top Papers
- 1