README.md

---
pretty_name: HumanClawBench HSSD val41 supplement
license: other
extra_gated_prompt: >-
  This supplement contains derivative, instance-specific meshes for HSSD-Hab
  scenes. By requesting access you confirm that you have authorized access to
  HSSD-Hab, that you comply with the applicable HSSD terms, and that you will
  use these files for research within those terms.
tags:
  - arxiv:2607.27180
  - habitat-sim
  - embodied-ai
  - benchmark
---

<div align="right"><a href="README_zh.md">中文</a></div>

# HumanClawBench HSSD val41 supplement

[**🌐 Project Page**](https://human-claw.github.io) | [**📄 Paper (arXiv:2607.27180)**](https://arxiv.org/abs/2607.27180) | [**💻 Code**](https://github.com/Human-CLAW/HumanCLAW) | [**🏋️ Motion weights**](https://huggingface.co/HumanCLAW/HumanCLAW)

[HumanCLAW](https://arxiv.org/abs/2607.27180) evaluates vision-language models
as full-body agents in 1,218 find–navigate–interact episodes across 41 HSSD
indoor scenes. This gated dataset ships **only the small mesh supplement**
those scenes need. It is not a copy of HSSD and cannot be used on its own.

## Step 1 — download the official HSSD data first

The benchmark scenes are built on the official Habitat-ready HSSD dataset
(`hssd-hab`, version 0.2.5). Request access there and download it before
using this supplement:

- **<https://huggingface.co/datasets/hssd/hssd-hab>** (gated; accept the HSSD
  terms on that page first)

Your download should contain:

```text
/path/to/hssd-hab/
├── hssd-hab.scene_dataset_config.json
├── objects/
├── stages/
└── semantics/
```

## Step 2 — what this supplement adds

Some scene instances cannot be loaded faithfully from the official meshes
alone. This supplement provides 1,693 instance-specific baked GLB meshes
that:

- bake per-instance **scale or reflection** into mesh vertices,
- repair **triangle winding** so Bullet collision behaves correctly, and
- preserve the benchmark cases that intentionally use the **render mesh as
  the exact collider** instead of a coarse proxy.

The underlying object geometry comes from HSSD; access is gated and users
must comply with the applicable HSSD terms.

## Files

```text
hssd/
├── humanclaw-hssd-val41-supplement-v1.tar.gz
└── humanclaw-hssd-val41-supplement-v1.manifest.json
```

The archive contains 1,693 content-addressed GLB blobs. Their logical size is
184,310,072 bytes (176 MiB); the compressed archive is 83,683,128 bytes.

```text
archive sha256: fd3422b302fcac6696903d73f3d04b54bf66e0603288f3631ca42dd6b4dc8ab2
```

The manifest maps every HumanClaw instance filename to its exact blob, size,
and SHA-256 digest. All 1,693 baked outputs have distinct content hashes.

## Step 3 — combine them (automatic)

After accepting access and authenticating with Hugging Face, HumanClawBench
downloads, verifies, and caches this archive automatically, then combines it
with your official HSSD download:

```bash
hf auth login
humanclaw-bench prepare-hssd --hssd-root /path/to/hssd-hab
```

The original HSSD tree is never modified. The prepared dataset symlinks the
official HSSD files and the verified cached supplement.

## Offline setup

Download the archive on a connected machine, transfer it to the evaluation
host, and pass it explicitly:

```bash
humanclaw-bench prepare-hssd \
  --hssd-root /path/to/hssd-hab \
  --supplement /path/to/humanclaw-hssd-val41-supplement-v1.tar.gz
```

Passing an already extracted directory containing `blobs/` is also supported.

## Citation

```bibtex
@article{siyao2026humanclaw,
  title   = {HumanCLAW: Can Vision-Language Models Act Through a Body?},
  author  = {Li, Siyao and Gu, Jiawei and Liu, Shuai and Hu, Kairui and Li, Zekun and
             Li, Linjie and Tang, Chengcheng and Wu, Po-Chen and Shugurov, Ivan and
             Ma, Lingni and Zollhoefer, Michael and An, Sizhe and Mittal, Abhay and
             Zhao, Amy and Krishna, Ranjay and Li, Manling and Liu, Ziwei and Guo, Chuan},
  journal = {arXiv preprint arXiv:2607.27180},
  year    = {2026}
}
```