AutoNeRF: An AI Approach Designed to Use Autonomous Agents to Generate Impl

AutoNeRF enables autonomous drones and robots to collect data required for training neural implicit representations of a scene.

During the Exploration Policy Training phase, an exploration policy is trained using intrinsic rewards in a set of training environments.

One of the key advantages of AutoNeRF is its ability to generate high-quality implicit map representations using data collected by autonomous agents.

Autonomous robots and drones typically have two primary phases: Exploration Policy Train and NeRF Training.

In this chapter, we focus on describing the first phase, which consists of training exploration policies to maximize coverage, find specific goals or objects, and support active learning.

Source: https://arxiv.org/pdf/2304.11241.02638-9216.AEZ

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