# LangLoc: "Tell Me What You See"

arXiv: https://arxiv.org/abs/2607.05077 · HTML: https://mlsanity.com/paper/2607.05077/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 6 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Setting up a Reinforcement Learning Task with a Real-World Robot](https://mlsanity.com/paper/1803.07067/) (2018)
- [Acting in Delayed Environments with Non-Stationary Markov Policies](https://mlsanity.com/paper/2101.11992/) (2021)
- [At Human Speed: Deep Reinforcement Learning with Action Delay](https://mlsanity.com/paper/1810.07286/) (2018)
- [Revisiting State Augmentation methods for Reinforcement Learning with Stochastic Delays](https://mlsanity.com/paper/2108.07555/) (2021)
- [Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays](https://mlsanity.com/paper/2402.03141/) (2024)

## Abstract (arXiv)

We tackle fine-grained indoor localization from natural language: given a free-form description of one's surroundings, estimate the observer's 2D position and heading within a known 3D environment. Language queries are lightweight, privacy-preserving, and need no camera - yet prior work stops at coarse scene retrieval and cannot resolve an intra-scene pose. We close this gap with LangLoc, a three-stage pipeline that (i) retrieves the correct scene via a dual-branch GATv2 encoder with CLIP semantic features, surpassing the previous best by 8 percentage points in Top-1 recall; (ii) estimates position and heading by scoring a dense floor grid through ray-cast object visibility, reaching a median error of 0.95 m; and (iii) resolves residual ambiguity through a Bayesian dialog module that asks targeted yes/no questions and updates a pose posterior until the location is pinpointed. To support this task we contribute a benchmark of 13,000+ pose-indexed natural-language descriptions over 1,300+ indoor 3D scans.