Less is More 🍋: Scalable Visual Navigation
from Limited Data

📄 Paper | 🌐 Project Page | 🤗 Weights | 📊 Dataset

Less is More (LiMo) is a transformer-based visual navigation policy that predicts goal-conditioned SE(2) trajectories from a single RGB observation. We demonstrate that augmenting limited expert demonstrations with geometric planner-generated trajectories yields substantial performance improvements, achieving robust visual navigation through strategic data curation rather than simply collecting more data.

Release Status

• Inference code
• Checkpoints released (SafeTensors on HuggingFace)
• Dataset and training code
• ROS integration
• Dataset builder with MPPI planner

Installation

This project uses uv for dependency management. We assume an NVIDIA GPU with driver version ≥530 (for CUDA 12.1 support).

git clone https://github.com/leggedrobotics/less-is-more \
  && cd less-is-more \
  && uv sync \
  && uv pip install -e .

The codebase uses Hydra for configuration management in the limo package.

Download Pretrained Weights

Download the pretrained LiMo checkpoint:

wget -O data/weights/limo_trained_on_D_aug.safetensors \
  https://huggingface.co/yv1es/less-is-more/resolve/main/limo_trained_on_D_aug.safetensors

Quick Start

Run inference on the provided examples:

uv run limo/src/inference.py

The default configuration in limo/configs/inference.yaml processes images from the example dataset in data/inference_example/.

Example: Inside ETH Zurich main building with multiple predictions

Example: Outdoor mountain trail with multiple predictions

Configuration

The inference pipeline is configured via limo/configs/inference.yaml:

• weights_path: Path to model weights (default: data/weights/limo_trained_on_D_aug.safetensors)
• input_path: Single image file or directory containing .jpg, .png, or .jpeg files
• goals_csv: CSV file defining navigation targets (format: x,y,yaw in meters/radians, robot frame)
• camera_info: Optional YAML file with camera intrinsics for path projection visualization
• output_dir: Directory for generated visualizations

See data/inference_example/ for a complete working example with sample images, goals, and camera calibration.

Training

The training pipeline uses PyTorch Lightning for model training and Hydra for configuration management.

Training Script

Train the model using: :

uv run limo/src/train.py experiment=train_limo_debug

(this debug experiment config will only start a quick debug run to test your system)

The main entry point is limo/src/train.py, which:

• Loads configuration from limo/configs/train.yaml and an experiment config
• Automatically pulls the dataset from HuggingFace
• Runs training with logging
• Saves checkpoints and weights

Configuration System

The training pipeline uses Hydra with the following structure:

Config directories (limo/configs/):

• data/: Dataset configuration (e.g., limo.yaml - LimoDataModule settings)
• model/: Model architecture (e.g., limo.yaml - network configuration)
• trainer/: PyTorch Lightning trainer settings
• logger/: Logging backends (e.g., wandb, csv)
• callbacks/: Training callbacks (e.g., checkpointing)
• experiment/: Complete experiment configs that override defaults

Experiment configs (limo/configs/experiment/):

• train_limo_debug.yaml: Quick debug run with 5 epochs, limited batches
• train_limo_on_D_aug.yaml: Full training on augmented dataset
• train_limo_side_cams.yaml: Training with side camera inputs

Override configs using the Hydra syntax:

# Use a specific experiment
uv run limo/src/train.py experiment=train_limo_on_D_aug

# Override specific parameters
uv run limo/src/train.py experiment=train_limo_debug data.batch_size=32 trainer.max_epochs=10

# Disable wandb logging
uv run limo/src/train.py experiment=train_limo_debug logger=null

By default, training uses Weights & Biases for logging.

Checkpoints and Weights

• Checkpoints: Saved to logs/train/runs/<timestamp>/checkpoints/ (PyTorch Lightning format)
• SafeTensors weights: Automatically converted and saved to logs/train/runs/<timestamp>/weights/ after each checkpoint

Dataset

LiMo's training data is based on Grand Tour dataset from HuggingFace: leggedrobotics/grand_tour_dataset We added LiMo's data to the same HuggingFace repo.

Note: The dataset is automatically pulled from HuggingFace. No manual download required!

Dataset Types

The Grand Tour dataset contains multiple mission recordings. Different sample types can be extracted:

• Teleoperation samples (tel): Expert demonstrations from human teleoperation
• Geometric samples (geo): Trajectories generated by the MPPI geometric planner
• Augmented samples (aug): Combined set of teleoperation + geometric samples

Select the dataset type using the get_dataset() method in dataset/src/limo_datset.py:

from dataset.src.limo_datset import get_dataset

# Load teleoperation samples only
dataset_tel = get_dataset(
    dataset_type="tel",           # or "geo", "aug"
    dataset_folder="data/dataset",
    missions_csv="missions_split.csv",
    with_side_cams=False
)

# Load augmented samples with side cameras
dataset_aug = get_dataset(
    dataset_type="aug",
    dataset_folder="data/dataset",
    missions_csv="missions_split.csv",
    with_side_cams=True
)

Missions and Train/Val/Test Split

The missions_split.csv file controls which missions are used and how they're split:

CSV Format:

Mission,Timestamp,Split
grandtour_mission_1,2024-01-15,train
grandtour_mission_2,2024-01-16,val
grandtour_mission_3,2024-01-17,test

Usage:

• Include only specific missions by adding rows to the CSV
• Control split ratios by adjusting the number of missions per split
• Quick debugging: Use a subset of missions (see limo/configs/data/missions_split_debug.csv)

Citation

If you use this work in your research, please cite:

@misc{inglin2026morescalablevisualnavigation,
      title={Less Is More: Scalable Visual Navigation from Limited Data},
      author={Yves Inglin and Jonas Frey and Changan Chen and Marco Hutter},
      year={2026},
      eprint={2601.17815},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2601.17815},
}