Autonomous mobile robots are required to move throughout map the environment, locate
themselves, and plan paths between positions. Vision stands out among the other senses …

Sensory processing of visual, auditory, and other sensor information (eg, visual imagery,
LIDAR, RADAR) is conventionally based on “stovepiped,” or isolated processing, with little …

Abstract Lifelong Deep Neural Network (L-DNN) technology revolutionizes Deep Learning
by enabling fast, post-deployment learning without extensive training, heavy computing …

Sensory processing of visual, auditory, and other sensor information (eg, visual imagery,
LIDAR, RADAR) is conventionally based on “stovepiped,” or isolated processing, with little …

The systems and methods disclosed herein include a path integration system that calculates
optic flow, infers angular velocity from the flow field, and incorporates this velocity estimate …

Existing work on Deep reinforcement learning-based visual navigation mainly focuses on
autonomous agents with ample power and compute resources. However, Reinforcement …

Today, artificial neural networks are trained on large sets of manually tagged images.
Generally, for better training, the training data should be as large as possible. Unfortunately …

Sensory processing of visual, auditory, and other sensor information (eg, visual imagery,
LIDAR, RADAR) is conventionally based on “stovepiped,” or isolated processing, with little …

The system and methods disclosed herein include a runtime architecture that takes a
nonspecific set of systems of differential equations, distributes them across the network, and …

Sensory processing of visual, auditory, and other sensor information (eg, visual imagery,
LIDAR, RADAR) is conventionally based on “stovepiped,” or isolated processing, with little …