# Sem-NaVAE: Semantically-Guided Outdoor Mapless Navigation via Generative Trajectory Priors

arXiv: https://arxiv.org/abs/2602.01429 · HTML: https://mlsanity.com/paper/2602.01429/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2026.

## Built on by (most cited first, 1 of 1)

- [Steering Generative Robot Policies with Lexicographic Preferences](https://mlsanity.com/paper/2609.15014/) (2026)

## Abstract (arXiv)

This work presents a mapless navigation approach for outdoor applications. It combines the exploratory capacity of conditional variational autoencoders (CVAEs) to generate trajectories and the semantic segmentation capabilities of a lightweight visual language model (VLM) to select the trajectory to execute. Open-vocabulary segmentation is used to score and select the generated trajectories based on natural language, and a state-of-the-art local planner executes velocity commands. One of the key features of the proposed approach is its ability to generate a large variability of trajectories and select them to navigate in real-time. In real-world outdoor experiments, Sem-NaVAE achieves a 90% success rate across routes of 120-240m in unseen environments, outperforming the nearest baseline by 10% while remaining within 7% of a map-based upper bound. A video showing an experimental run of the system can be found in https://youtu.be/i3R5ey5O2yk.