Papers
41
Total Citations
668
H-Index
14
About
Jen Jen Chung is a robotics researcher whose work spans autonomous systems, informative path planning, multi-robot coordination, and robot manipulation. She is perhaps best known for her contributions to UAV-based terrain monitoring, where her 2020 framework for informative path planning (161 citations) established foundational methods for efficient data acquisition in complex environments. Building on this, her work on online 3D surface information gathering demonstrates a commitment to making aerial robots more adaptive and practically deployable. Chung has also made significant strides in robot manipulation, contributing the Volumetric Grasping Network for real-time 6-DOF grasp detection in cluttered scenes, and developing closed-loop next-best-view planning for targeted grasping tasks. Her research extends into multi-agent systems, where she tackled the challenge of structural credit assignment in tightly coupled robot teams and developed dynamic traffic management strategies using multiagent learning. Additional contributions include risk-aware graph search under uncertainty and distributed positioning coverage using multi-robot systems. More recently, her comparative analysis of Behavior Trees and Finite State Machines offers practical guidance for robotics programmers. Collectively, her body of work, accumulating over 440 citations, reflects a broad and impactful research agenda advancing intelligent, adaptive autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1An informative path planning framework for UAV-based terrain monitoring161 citations · 2020
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- 3Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Clutter44 citations · 2021
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- 7Risk-aware graph search with dynamic edge cost discovery26 citations · 2018
- 8Closed-Loop Next-Best-View Planning for Target-Driven Grasping22 citations · 2022
- 9A multiagent framework for learning dynamic traffic management strategies22 citations · 2018
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