Jiacheng Yang
Papers
2
Total Citations
9
H-Index
2
About
Jiacheng Yang is pushing the boundaries of intelligent robotics at the intersection of computer vision, motion planning, and deep reinforcement learning. His research centers on equipping autonomous systems with the perceptual and decision-making capabilities needed to operate in complex, unstructured environments. Yang’s most notable contribution is the **3D-OAS** framework, an end-to-end system for vision-guided top-down parcel bin-picking. By integrating 3D overlapping-aware instance segmentation with a Graph Neural Network (GNN), his work enables robots to handle cluttered, stacked objects with remarkable precision—a critical advance for logistics and warehouse automation. This flagship paper has already garnered **6 citations** since its 2023 publication. Complementing this, Yang addresses the challenge of mobile robot navigation in dynamic spaces through his work on **trajectory prediction and reinforcement learning** (3 citations). His approach moves beyond traditional offline replanning, allowing robots to anticipate and react to moving pedestrians and other agents in real-time. By fusing robust 3D perception with adaptive, learning-based control, Jiacheng Yang is crafting the foundational technologies for the next generation of agile, perceptive, and truly autonomous robots.
Research Focus
Key Achievements
Top Papers
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- 2