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

arXiv: https://arxiv.org/abs/2203.00549 · HTML: https://mlsanity.com/paper/2203.00549/

## Key facts

- Cited by 11 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 1 Mar 2022.

## Built on by (most cited first, 11 of 11)

- [Semantic Image Segmentation: Two Decades of Research](https://mlsanity.com/paper/2302.06378/) (2023)
- [An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping](https://mlsanity.com/paper/2302.03347/) (2023)
- [SC-Explorer: Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning](https://mlsanity.com/paper/2208.08307/) (2022)
- [Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning](https://mlsanity.com/paper/2312.04402/) (2023)
- [SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene Understanding](https://mlsanity.com/paper/2206.10670/) (2022)
- [Learning to Explore Informative Trajectories and Samples for Embodied Perception](https://mlsanity.com/paper/2303.10936/) (2023)
- [Active Visual Localization for Multi-Agent Collaboration: A Data-Driven Approach](https://mlsanity.com/paper/2310.02650/) (2023)
- [Interactive Semantic Map Representation for Skill-based Visual Object Navigation](https://mlsanity.com/paper/2311.04107/) (2023)
- [Relative Difficulty Distillation for Semantic Segmentation](https://mlsanity.com/paper/2407.03719/) (2024)
- [Box Pose and Shape Estimation and Domain Adaptation for Large-Scale Warehouse Automation](https://mlsanity.com/paper/2507.00984/) (2025)
- [SegmATRon: Embodied Adaptive Semantic Segmentation for Indoor Environment](https://mlsanity.com/paper/2310.12031/) (2023)

## Abstract (arXiv)

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.