When training a neural network for a desired task, one may prefer to adapt a pretrained network rather than start with a randomly initialized one -- due to lacking enough training data, performing lifelong learning where the system has to learn a new task while being previously trained for other tasks, or wishing to encode priors in the network via preset weights.
The results of this method, called invariance regularization, show an improvement in the generalization of policies to environments not seen during training.
In this paper, we present a novel Hamilton-Jacobi (HJ) reachability-based method to generate supervision for the CNN for waypoint prediction.
We find that SRCC for Habitat as used for the CVPR19 challenge is low (0. 18 for the success metric), which suggests that performance improvements for this simulator-based challenge would not transfer well to a physical robot.
Visual navigation is a task of training an embodied agent by intelligently navigating to a target object (e. g., television) using only visual observations.
An agent solving tasks in a HANNA environment can leverage simulated human assistants, called ANNA (Automatic Natural Navigation Assistants), which, upon request, provide natural language and visual instructions to direct the agent towards the goals.
Our first contribution is the creation of a large-scale dataset with verbal navigation instructions.