# Embodied Active Domain Adaptation for Semantic Segmentation via Informative Path Planning

Source: [Embodied Active Domain Adaptation for Semantic Segmentation via Informative Path Planning](<https://www.sota2.com/research/paper/68fa6cf11e209131cea36afd>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:21:36.840Z

Authors: Ren\\'e Zurbr\\"ugg, Hermann Blum, Cesar Cadena, Roland Siegwart, Lukas Schmid

Published: 2022-03-01T15:32:11.000Z

## Abstract

This work presents an embodied agent that can adapt its semantic segmentation network to new indoor environments in a fully autonomous way. Because semantic segmentation networks fail to generalize well to unseen environments, the agent collects images of the new environment which are then used for self-supervised domain adaptation. We formulate this as an informative path planning problem, and present a novel information gain that leverages uncertainty extracted from the semantic model to safely collect relevant data. As domain adaptation progresses, these uncertainties change over time and the rapid learning feedback of our system drives the agent to collect different data. Experiments show that our method adapts to new environments faster and with higher final performance compared to an exploration objective, and can successfully be deployed to real-world environments on physical robots.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2203.00549>)

[PDF](<https://arxiv.org/pdf/2203.00549.pdf>)