LAP-3B is a Vision-Language-Action (VLA) model trained using Language-Action Pre-Training (LAP), which represents actions as language-actions, allowing the model to preserve semantic reasoning capabilities from large vision-language models while learning robot control.
This design enables strong zero-shot transfer across robot embodiments, allowing the same model to generalize to different robots without architecture changes or embodiment-specific fine-tuning.
For model and training details, please refer to our paper.
Key Capabilities
LAP-3B demonstrates strong cross-embodiment generalization across multiple real robot platforms.
Highlights from the paper:
>50% average zero-shot success rate on unseen robots
~2× improvement over prior VLA models on cross-embodiment benchmarks
Successful deployment on multiple robot platforms, including:
Franka Panda
Kinova
YAM
DROID
Supported manipulation tasks include:
pick and place
object sorting
container placement
towel manipulation
Limitations
While LAP-3B demonstrates strong cross-embodiment transfer, several limitations remain:
Current experiments primarily focus on single-arm manipulation.
Performance may degrade in settings involving highly dexterous manipulation or rich visual distractors.
Future work includes extending LAP to more complex embodiments and tasks, including:
bimanual robots
dexterous hands
mobile manipulation systems
Intended Use
LAP-3B is intended for:
research in robot learning
vision-language-action models
cross-embodiment policy learning
manipulation policy research
The model is not intended for safety-critical deployments without additional validation.
Citation
If you use LAP-3B in your research, please cite:
bibtex
1@article{zha2026lap,
2 title={LAP: Language-Action Pre-Training Enables Zero-Shot Cross-Embodiment Transfer},
3 author={Zha, Lihan and Hancock, Asher and Zhang, Mingtong and Yin, Tenny and Huang, Yixuan and Shah, Dhruv and Ren, Allen Z. and Majumdar, Anirudha},
4 journal={arXiv preprint arXiv:2602.10556},
5 year={2026}
6}
