Papers
11
Total Citations
101
H-Index
5
About
Marco Monforte is a leading researcher at the intersection of neuromorphic engineering and robotics, focusing on how brain-inspired hardware can enable faster, more adaptive autonomous agents. His work centers on three key areas: closed-loop spiking control on neuromorphic processors, event-based vision for high-speed perception, and learning algorithms for robotic manipulation. Monforte’s most impactful contribution is his 2020 paper on closed-loop spiking control implemented on the iCub humanoid robot (36 citations), which demonstrated how neuromorphic processors can achieve low-latency, low-power control loops essential for real-time interaction. He has also pioneered the use of event cameras for trajectory prediction, notably in fast-changing environments like air-hockey (14 citations), and developed novel inverse kinematics learning methods that overcome the low precision of neuromorphic hardware (17 citations). His work on multifunctional principal component analysis for human-like grasping (11 citations) further showcases his ability to bridge computational efficiency with biological plausibility. Monforte’s research has direct implications for autonomous systems requiring split-second reactions, such as catching balls or playing air-hockey, and his ongoing exploration of spatiotemporal prediction promises to push the boundaries of what neuromorphic robots can achieve.
Research Focus
Key Achievements
Top Papers
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- 5How Late is too Late? A Preliminary Event-based Latency Evaluation7 citations · 2022
- 6Multifunctional Principal Component Analysis for Human-Like Grasping4 citations · 2018
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