VioLA: Learning Generalist Humanoid Control Policies from Human Data
VioLA: Learning Generalist Humanoid Control Policies from Human Data
This paper presents VioLA, a generalist humanoid policy that predicts body and hand motion latents instead of joint commands. By mapping human and robot motion into the same latent space, VioLA enables zero-shot task execution on real robots, achieving 100% success in locomotion and 88.6% in manipulation without task-specific fine-tuning. The method is validated across multiple models and datasets.
VioLA introduces a novel approach to humanoid control by predicting motion latents instead of joint commands, enabling zero-shot task execution on real robots without fine-tuning. The method leverages human demonstrations to overcome the scarcity of robot data, achieving high success rates in both locomotion and manipulation tasks.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
Frames
140,600,000 frames
- code: not_reported
- weights: not_reported
- dataset: not_reported
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
ler execute these latents on the robot. Their corresponding motion encoders map human and robot motion into the same latent spaces. A human recording is therefore labeled in the policy's action space, and the training demonstration pool contains 140.6 million frames, 93.2% of them human. As a result, VioLA follows locomotion instructions on the real robot zero-shot, without task-specific fine-tuning, reaching 100% su
Open source S4
ccess where GR00T N1.7 and $\Psi_0$ reach 16.7% and 0%, respectively. It also reaches 88.6% manipulation success without task-specific fine-tuning. The same approach works across two VLA and one world-action model backbones. A generalist policy trained on human demonstrations alone performs locomotion tasks on the real robot zero-shot. Code and checkpoints will be released.
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org