HumanCLAW is an evaluation framework that decouples a VLM's action decision-making from low-level motor execution. At every sub-second step, a harnessed, off-the-shelf VLM issues an atomic whole-body skill; the skill is realized as continuous full-body motion with real physical consequences—contact, collision, and gravity—while balance and motor-tracking failures are factored out. What remains measurable is the model's action intelligence: its moment-to-moment choice of what the body should execute next.
On top of the framework, HumanCLAW-Bench provides 1,218 long-horizon, egocentric find–navigate–interact episodes across 41 indoor scenes. Across nine state-of-the-art VLMs, none solves the benchmark—the best model reaches only a 16.8% success rate. What current VLMs lack is embodied self-awareness: they lose track of the body they control—where it is, whether it has arrived, and when it has collided with the world.
The code and benchmark are being prepared for release. Stay tuned!
- HumanCLAW harness (VLM skill interface, verifier, motion generator, half-physics simulator)
- Motion generator weights (skill-conditioned motion prior + per-skill adapters)
- Half-physics simulation environment
- HumanCLAW-Bench episodes and evaluation metrics
- Leaderboard submission guide
In the meantime, see the project page for demos, the failure-analysis gallery, and the leaderboard.
If you find HumanCLAW useful, please cite:
@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}
}