Cross-Modal Learning Filters for RGB-Neuromorphic Wormhole Learning

Robots that need to act in an uncertain, populated, and varied world need heterogeneous sensors to be able to perceive and act robustly. For example, self-driving cars currently on the road are equipped with dozens of sensors of several types (lidar, radar, sonar, cameras, . . . ). All of this existing and emerging complexity opens up many interesting questions regarding how to deal with multi-modal perception and learning.