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Map Prediction and Generative Entropy for Multi-Agent Exploration by Spinos, Alexander; Woosley, Bradley; Rokisky, Justin; Korpela, Christopher; Rogers, John G.; Bittner, Brian A. is a scholarly article available to read on EtoBox.

What is Map Prediction and Generative Entropy for Multi-Agent Exploration about?

Traditionally, autonomous reconnaissance applications have acted on explicit sets of historical observations. Aided by recent breakthroughs in generative technologies, this work enables robot teams to act beyond what is currently known about the environment by inferring a distribution of reasonable interpretations of the scene. We developed a map predictor that inpaints the unknown space in a multi-agent 2D occupancy map during an exploration mission. From a comparison of several inpainting methods, we found that a fine-tuned latent diffusion inpainting model could provide rich and coherent interpretations of simulated urban environments with relatively little computation time. By iteratively inferring interpretations of the scene throughout an exploration run, we are able to identify areas that exhibit high uncertainty in the prediction, which we formalize with the concept of generative entropy. We prioritize tasks in regions of high generative entropy, hypothesizing that this will expedite convergence on an accurate predicted map of the scene. In our study we juxtapose this new paradigm of task ranking with the state of the art, which ranks regions to explore by those which maxim

Author

Spinos, Alexander; Woosley, Bradley; Rokisky, Justin; Korpela, Christopher; Rogers, John G.; Bittner, Brian A.

Published

2025

Language

EN