Massimiliano Iacono
Papers
11
Total Citations
257
H-Index
8
About
Massimiliano Iacono is a robotics and computer vision researcher whose work sits at the intersection of neuromorphic sensing, event-driven perception, and autonomous robot control. He is best known for pioneering the application of event cameras — biologically inspired visual sensors offering ultra-low latency, high dynamic range, and efficient data compression — to real-world robotic systems, amassing over 250 citations across his published work. Iacono's most impactful contributions include adapting off-the-shelf deep learning frameworks for event-driven object detection (72 citations) and developing the Event-Driven Software Library for YARP, enabling researchers to deploy neuromorphic algorithms on the iCub humanoid robot platform. His early work on UAV navigation using depth cameras for path following and obstacle avoidance (83 citations) demonstrated strong cross-domain versatility. More recently, he has advanced proto-object-based visual saliency in both 2D and 3D space, fast trajectory prediction for reactive robot control, and neuromorphic attentional systems implemented on SpiNNaker hardware. His research on interactive continual learning further positions him at the frontier of cognitive robotics. Collectively, Iacono's work meaningfully bridges low-power neuromorphic hardware with practical robotic perception challenges.
Research Focus
Key Achievements
Top Papers
- 1
- 2Towards Event-Driven Object Detection with Off-the-Shelf Deep Learning72 citations · 2018
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- 5Proto-object based saliency for event-driven cameras14 citations · 2019
- 6
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- 8Interactive continual learning for robots: a neuromorphic approach11 citations · 2022
- 9
- 10Hybrid Object Tracking with Events and Frames4 citations · 2023