SERF-VLA

SERF-VLA BEHAVIOR-1K Checkpoints

Official checkpoint release for:

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

[arXiv] [Website] [Code]

Overview

This repository contains PI0.5 baseline and SERF-VLA policy checkpoints for BEHAVIOR-1K experiments.

It includes:

• PI0.5 baseline checkpoints fine-tuned on individual BEHAVIOR-1K tasks
• SERF-VLA checkpoints conditioned on 4D spatiotemporal environment and robot feature maps

These checkpoints are for the policy learning component only. The SERF mapping component is not included in this repository.

Checkpoint Initialization

All released checkpoints were initialized from the PI0.5 checkpoint pretrained on 50 BEHAVIOR-1K tasks:

• IliaLarchenko/behavior_50t_checkpoint

This checkpoint was released by the first-place solution of the 2025 BEHAVIOR Challenge and was used as the initialization for both the PI0.5 baseline checkpoints and the SERF-VLA checkpoints in this repository.

Checkpoints

Each checkpoint follows the original policy checkpoint structure:

checkpoint_name/
├── assets/
└── params/

Usage

Download all checkpoints with huggingface_hub:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="byeonghyunpak/SERF-VLA",
    repo_type="model",
    local_dir="checkpoints/serf-vla-behavior-b1k",
)

To download a specific checkpoint folder only:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="byeonghyunpak/SERF-VLA",
    repo_type="model",
    local_dir="checkpoints/serf-vla-behavior-b1k",
    allow_patterns=[
        "pi_serf_behavior_b1k_fast--4d_env_robot_feat_map--50t_lora--task-0021/**"
    ],
)

For installation, data preparation, training, and evaluation instructions, please refer to the official code repository:

https://github.com/ExistentialRobotics/SERF-VLA

Note

BEHAVIOR-1K evaluation is non-deterministic; results can differ across repeated runs due to variability in the underlying physics simulation and error accumulation over long execution time.

Citation

If you find these checkpoints useful, please cite 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}
}

Acknowledgements

This release builds on behavior-1k-solution, openpi, and BEHAVIOR-1K. We thank the authors and maintainers of these projects.