Dataset Card for NaviTrace

NaviTrace is a Visual Question Answering benchmark for evaluating how well vision-language models (VLMs) understand embodiment-specific navigation in real-world scenes. Each sample presents a first-person image of an outdoor environment paired with a natural language navigation instruction. The task is to predict a 2D navigation trace — a sequence of waypoints in image space — that a given embodiment type would follow to complete the instruction.

The dataset contains 1,002 scenarios with over 3,000 expert-annotated traces across four embodiment types: Human, Legged Robot, Wheeled Robot, and Bicycle.

This is a FiftyOne dataset with 1002 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/NaviTrace")

# Launch the App
session = fo.launch_app(dataset)

Dataset Sourced

• Curated by: Tim Windecker, Manthan Patel, Moritz Reuss, Richard Schwarzkopf, Cesar Cadena, Rudolf Lioutikov, Marco Hutter, Jonas Frey (ETH Zurich, KIT, FZI)
• Language: English
• Repository: leggedrobotics/navitrace
• Paper: NaviTrace: Evaluating Embodied Navigation of Vision-Language Models (arXiv:2510.26909)
• Leaderboard: leggedrobotics.github.io/navitrace_webpage

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Uses

Direct Use

• Benchmarking VLMs on embodied navigation trace prediction
• Evaluating spatial grounding and goal localization in vision-language models
• Studying how embodiment type affects navigation decisions (e.g., stairs vs. ramps for wheeled vs. legged robots)
• Fine-tuning models on the validation split for navigation-aware reasoning

Out-of-Scope Use

• Real-time robot control or closed-loop navigation — traces are 2D image-space paths, not 3D or metric coordinates
• Temporal or multi-step planning — each scenario is a single static image
• Aerial embodiments — only ground-level embodiments are covered

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Embodiments

Each scenario annotates traces for one or more of the following embodiment types:

Embodiment	Description
Human	Standard pedestrian; can use stairs and ramps
Legged Robot	Quadruped (e.g., ANYmal); similar to human but shorter
Wheeled Robot	Delivery robot; prefers smooth surfaces and ramps, cannot use stairs
Bicycle	Cyclist; follows traffic rules, avoids stairs, prefers bike lanes

Task Categories

Scenarios are tagged with one or more of the following navigation challenge categories:

• Geometric Terrain — decisions based on 3D structure (stairs, cliffs, closed doors)
• Semantic Terrain — decisions requiring semantic understanding (sidewalk vs. road, terrain stiffness)
• Accessibility — barrier-free access concerns (ramps, automatic doors)
• Visibility — occlusions, poor lighting, or ambiguous information
• Social Norms — normative constraints from rules or signage (crosswalks, pedestrian-only zones)
• Dynamic Obstacle Avoidance — moving obstacles such as pedestrians or vehicles
• Stationary Obstacle Avoidance — fixed obstacles like debris or road closures

Scene Metadata

Each scenario includes scene-level metadata covering geographic location, environment type, lighting, and weather. The dataset is geographically concentrated in Switzerland (including Zurich and ETH campus), with additional samples from the USA, Japan, Germany, South Korea, Sweden, Spain, Portugal, Austria, England, and Italy. Faces and license plates are anonymized using EgoBlur.

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FiftyOne Structure

Sample Fields

Field	FiftyOne Type	Description
filepath	StringField	Path to the JPEG image (1365×1024 px)
split	StringField	"validation" or "test"
sample_id	StringField	UUID identifying the scenario
task	StringField	Navigation instruction (e.g., "Go to the garden")
embodiments	ListField(StringField)	Embodiment types present in this scenario
category	ListField(StringField)	Navigation challenge categories for this scenario
context	StringField	Free-text scene description written by annotators
city	StringField	City where the image was captured
country	StringField	Country where the image was captured
lighting_conditions	StringField	Daylight, Low Light, Night, or Indoor Lighting
natural_structured	StringField	Natural, Structured, or Mixed
task_type	StringField	Goal (reach a location) or Direction (follow a path)
urban_rural	StringField	Urban, Rural, or Mixed
weather_conditions	StringField	Clear, Cloudy, Rainy, Snowy, Foggy, or Unknown

Label Fields

Field	FiftyOne Type	Description
segmentation_mask	fo.Segmentation	Per-pixel semantic class mask (Mask2Former on Mapillary Vistas)
human	fo.Polylines	Ground-truth navigation trace(s) for a Human
legged_robot	fo.Polylines	Ground-truth navigation trace(s) for a Legged Robot
wheeled_robot	fo.Polylines	Ground-truth navigation trace(s) for a Wheeled Robot
bicycle	fo.Polylines	Ground-truth navigation trace(s) for a Bicycle
has_human	BooleanField	Whether a human trace is present
has_legged_robot	BooleanField	Whether a legged robot trace is present
has_wheeled_robot	BooleanField	Whether a wheeled robot trace is present
has_bicycle	BooleanField	Whether a bicycle trace is present

Polyline Details

Each fo.Polylines label field may contain one or more fo.Polyline objects. Multiple polylines within a field represent equally valid alternative paths annotated for the same scenario (annotator-provided alternatives). Points are normalized to [0, 1] in (x, y) image coordinates, where (0, 0) is the top-left and (1, 1) is the bottom-right. All traces originate near the bottom-center of the image ([0.5, 0.95]), representing the embodiment's start position. Polylines have closed=True and filled=False.

Trace Coverage

Field	Samples with traces
human	500 / 1002 (validation split)
legged_robot	501 / 1002
wheeled_robot	276 / 1002
bicycle	232 / 1002

Note: coverage reflects the validation split, as test split ground truths are withheld.

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Citation

@article{windecker2025navitrace,
  title={NaviTrace: Evaluating Embodied Navigation of Vision-Language Models},
  author={Windecker, Tim and Patel, Manthan and Reuss, Moritz and Schwarzkopf, Richard and Cadena, Cesar and Lioutikov, Rudolf and Hutter, Marco and Frey, Jonas},
  journal={arXiv preprint arXiv:2510.26909},
  year={2025}
}