Jacques Kaiser
Papers
20
Total Citations
427
H-Index
11
About
Jacques Kaiser is a pioneering researcher at the intersection of neuroscience and robotics, with a focus on neuromorphic computing, spiking neural networks (SNNs), and bio-inspired sensorimotor control. His work addresses a fundamental challenge in modern robotics: how biological principles of neural computation can be harnessed to create more efficient, adaptive machines. Kaiser's most influential contribution, the Neurorobotics Platform (2017, 117 citations), established a landmark simulation framework connecting biologically realistic brain models to robotic embodiments, enabling rigorous validation of computational neuroscience models in rich sensory environments. His comprehensive survey on neuromorphic stereo vision (2019, 63 citations) became an essential reference for researchers exploring event-driven depth perception using bio-inspired sensors. Across multiple studies, Kaiser has demonstrated that SNNs can effectively drive complex robotic behaviors — from anthropomorphic grasping and arm control to six-legged locomotion and EMG-triggered finger reflexes — without relying on traditional motion planning. His research on visual prediction learning further bridges cognitive neuroscience and robot learning. With over 350 cumulative citations, Kaiser's body of work represents a cohesive and impactful effort to translate biological intelligence into practical, neuromorphic robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Neuromorphic Stereo Vision: A Survey of Bio-Inspired Sensors and Algorithms63 citations · 2019
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- 5Soft-Grasping With an Anthropomorphic Robotic Hand Using Spiking Neurons23 citations · 2020
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- 10Microsaccades for Neuromorphic Stereo Vision13 citations · 2018