About

Akansel Cosgun is a robotics researcher whose work spans robotic manipulation, human-robot interaction, semantic understanding, and robot navigation. His most influential contribution is a comprehensive review of deep learning approaches to grasp synthesis (2023, 215 citations), which has quickly become a key reference for researchers tackling the complex challenge of robotic object grasping. Complementing this, his survey on semantics for robotic mapping, perception, and interaction (2020, over 100 citations) has shaped how the field thinks about endowing robots with richer world understanding. Cosgun's research also bridges the gap between robots and the humans they work alongside. His early work on autonomous person-following for telepresence robots (2013, 86 citations) demonstrated practical human-aware navigation, while later contributions explored anticipatory path planning and tactile belt interfaces for human guidance. More recently, he has advanced human-robot collaboration through augmented reality tools—visualizing robot intent during handovers and implementing virtual safety barriers in manufacturing settings. His 2024 review of differentiable simulators reflects a growing interest in physics-informed learning. Across these diverse threads, Cosgun's research consistently pursues robots that are safer, more perceptive, and more naturally integrated into human environments.

Research Focus

Key Achievements

14
H-Index
37
Papers
839
Total Citations
23
Avg Citations/Paper
🏆 Most Cited Paper
Deep Learning Approaches to Grasp Synthesis: A Review
215 citations · 2023
📈 Most Prolific Year: 2022 (11 Papers)
🤝 Key Collaborators: 71
🏛 Institutions: Deakin University, Monash University, Georgia Institute of Technology, Australian Regenerative Medicine Institute, Australian Centre for Robotic Vision

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago