Dustin Aganian
Papers
9
Total Citations
70
H-Index
5
About
Dustin Aganian is a computer vision and robotics researcher whose work sits at the intersection of human perception, action recognition, and human-robot collaboration. His research focuses on equipping autonomous systems — both mobile service robots and collaborative industrial robots (cobots) — with the perceptual intelligence needed to understand and interact with humans in real-world environments. Aganian's most-cited work, "A Multi-Modal Person Perception Framework for Socially Interactive Mobile Service Robots" (2020, 18 citations), established a foundational approach to multi-sensor fusion and human re-identification for service robotics. He has since made significant contributions to industrial human-robot collaboration, including the release of the ATTACH Dataset (2023, 17 citations), a dedicated benchmark for two-handed assembly action understanding that has quickly become a valuable resource for the research community. His follow-up studies on skeleton-based action recognition — incorporating object context and hand-body skeleton fusion — further advanced the state of the art in assembly task understanding. Beyond human perception, Aganian has contributed to robotic grasping through work on label encoding, uncertainty estimation, and object-agnostic grasp tracking. With over 70 cumulative citations across a focused and cohesive body of work, he represents an emerging voice in applied robotics perception research.
Research Focus
Key Achievements
Top Papers
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- 4A Little Bit Attention Is All You Need for Person Re-Identification7 citations · 2023
- 5Fusing Hand and Body Skeletons for Human Action Recognition in Assembly5 citations · 2023
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- 8GraspTrack: Object and Grasp Pose Tracking for Arbitrary Objects2 citations · 2024
- 9A Little Bit Attention Is All You Need for Person Re-Identification2 citations · 2023