Xuelin Chen
Papers
1
Total Citations
25
H-Index
1
About
Xuelin Chen is a researcher advancing the frontier of robotic manipulation through perception and interaction with 3D articulated objects. His work focuses on enabling robots to understand and operate everyday objects like cabinets, drawers, and doors—items that are ubiquitous in human environments yet notoriously difficult for machines to handle due to their diverse shapes, joints, and motion constraints. Chen’s most cited paper, “VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects” (2021, 25 citations), introduces a novel framework that learns to propose actionable visual trajectories directly from 3D observations, bypassing the need for explicit kinematic models. This contribution is pivotal for home-assistant robots, as it bridges the gap between perception and physical interaction with complex articulated structures. By tackling the rich variability in semantic categories and geometry, Chen’s work lays essential groundwork for more adaptive and intelligent robotic systems. His research holds promise for real-world applications in assistive robotics and automated home services, making him a notable emerging voice in embodied AI and 3D scene understanding.
Research Focus
Key Achievements
Top Papers
- 1