SERF-mapping

Official source code repository for:

SERF: Spatiotemporal Environment and Robot Feature Map for Long-Horizon Mobile Manipulation

[arXiv] [Website] [Video]

This repository contains code for learning, updating, and visualizing SERF environment and robot feature maps. It is focused on the mapping part only.

Note: this repository does not include the VLA component of SERF.

Installation

Run these commands from the repository root.

git submodule update --init --recursive
mamba create -n serf-mapping python=3.10 -y
conda activate serf-mapping
python -m pip install --upgrade pip setuptools wheel
python -m pip install -r requirements.txt

Map Feature Learning

Dataset Preparation

Download the pre-generated datasets from suk063/SERF. The full dataset can be large, so we recommend downloading only the files you need.

For example, download the robot dataset and one task-0021 training episode into data/:

hf download suk063/SERF \
  --repo-type dataset \
  --include "mapping_dataset/robot/robot_data.hdf5" \
  --include "mapping_dataset/task-0021/train/episode_00212800.hdf5" \
  --local-dir data

To generate the dataset yourself, see DATASET_GENERATION.md.

Extract DINO Embeddings

Download DINOv3 weights from facebookresearch/dinov3:

• --backbone_weights: dinov3_vitl16_pretrain_lvd1689m-8aa4cbdd.pth
• --dinotxt_weights: dinov3_vitl16_dinotxt_vision_head_and_text_encoder-a442d8f5.pth

Save the weights locally and pass their paths to the command below.

Add DINOv3 features to every HDF5 file under data/mapping_dataset:

python dataset/generate_dino_embedding.py \
  --input_path data/mapping_dataset \
  --backbone_weights /path/to/dinov3_vitl16_pretrain_lvd1689m-8aa4cbdd.pth \
  --dinotxt_weights /path/to/dinov3_vitl16_dinotxt_vision_head_and_text_encoder-a442d8f5.pth

Map Training

After extracting DINO features, train the environment and robot feature maps:

python mapping/train_neural_points_env_and_robot.py \
  --config mapping/config/config_env_and_robot.yaml

The command above jointly trains the environment and robot maps. To train them separately, use mapping/train_neural_points_env.py or mapping/train_neural_points_robot.py. Hyperparameters are in mapping/config/.

Download Pre-trained Maps

Pre-trained map models are available under map_models/ in suk063/SERF. For task-0021, download the model to data/map_models/task-0021:

hf download suk063/SERF \
  --repo-type dataset \
  --include "map_models/task-0021/**" \
  --local-dir data

Map Update

Download Expert Demonstrations

We provide one uncompressed expert demonstration per task, generated by replaying BEHAVIOR-1K demonstrations.

For example, download the replayed task-0021 demonstration:

hf download suk063/SERF \
  --repo-type dataset \
  --include "demonstration_replay/task-0021/episode_00212800.hdf5" \
  --local-dir data

See DATASET_GENERATION.md to replay demonstrations yourself.

Environment Map Update

Use the replayed expert demonstration to update the environment map:

python tracking/neural_point_tracking.py --task 0021

This offline tracker calls CoTracker at each step, making it slower than the online SERF VLA tracker but useful for offline tracking quality. For faster inference, refer to the SERF VLA tracking implementation.

Download Tracked Maps

Tracked environment maps are available under exported_neural_points/. For example, download the task-0021 tracked map:

hf download suk063/SERF \
  --repo-type dataset \
  --include "exported_neural_points/task-0021/**" \
  --local-dir data

Visualize Feature Map

To include the robot map overlay, download the matching BEHAVIOR-1K demonstration parquet file:

hf download behavior-1k/2025-challenge-demos \
  --repo-type dataset \
  --include "data/task-0021/episode_00212800.parquet" \
  --local-dir data/behavior-1k/2025-challenge-demos

Then visualize the feature map with the robot overlay:

python visualization/visualize_feature_map.py \
  --tracking_hdf5 data/exported_neural_points/task-0021/train/episode_00212800.hdf5 \
  --include_robot_model

Controls: T/R jump +100/-100 steps; F/D step +1/-1 frame.

Citation

If you find this repository useful, please consider citing our paper:

@article{kim2026serf,
  title = {SERF: Spatiotemporal Environment and Robot Feature Map for Long-Horizon Mobile Manipulation},
  author = {Kim, Sunghwan and Pak, Byeonghyun and Long, Kehan and Tian, Yulun and Atanasov, Nikolay},
  journal = {arXiv preprint arXiv:2606.12956},
  year = {2026}
}

License

This project is released under the license provided in LICENSE.

Acknowledgements

We thank the BEHAVIOR-1K, DINOv3, CoTracker3, and SAM2 teams for making their resources, models, and code publicly available.