zaleni/WSA-Large

WSA Model Collection

WSA₁: a 3D-Centric World-Spatial-Action Model for Generalizable Robot Control

Paper | Project Page | Code | Model Collection

WSA is a robot foundation model built on the 3D-centric World-Spatial-Action modeling paradigm. It jointly learns instruction-aligned 2D visual planning, action-conditioned 3D world modeling, and 3D-aware action generation. The released family contains 3B WSA-Base and 6B WSA-Large checkpoints for downstream fine-tuning, RoboTwin2.0, and LIBERO.

Released Models

Model	Size	Checkpoint type	Intended use
WSA-Base	3B	Pretrained	Initialization for downstream fine-tuning
WSA-Base-RoboTwin	3B	Fine-tuned	RoboTwin2.0 evaluation and inference
WSA-Base-LIBERO	3B	Fine-tuned	LIBERO evaluation and inference
WSA-Large	6B	Pretrained	Initialization for downstream fine-tuning
WSA-Large-RoboTwin	6B	Fine-tuned	RoboTwin2.0 evaluation and inference
WSA-Large-LIBERO	6B	Fine-tuned	LIBERO evaluation and inference

WSA-Base uses Qwen3-VL-2B-Instruct as its backbone. WSA-Large uses Wan2.2-TI2V-5B as its backbone.

Results

RoboTwin2.0

Average success rate on the randomized (hard) setting over 50 simulated ALOHA manipulation tasks:

Model	Average Success (Hard)
WSA1-B	92.70%
WSA1-L	93.14%

LIBERO

Success rates (%) on the four LIBERO task suites:

Method	LIBERO-Spatial	LIBERO-Object	LIBERO-Goal	LIBERO-10	Average
WSA1-B	98.6	99.6	97.2	94.2	97.4
WSA1-L	99.4	99.8	98.0	95.6	98.2

Refer to the paper and project page for the complete experimental setup, baseline details, and real-world results.

Citation

If you use WSA in your research, please cite:

@misc{jiang2026wsa1,
  title         = {WSA$_1$: a 3D-Centric World-Spatial-Action Model for Generalizable Robot Control},
  author        = {Jiahao Jiang and Jianing Zhang and Zhenhan Yin and Ruidong Chen and Sen Wang and Zhaoshu Yu and Pengpeng Zeng and Xiaofeng Cao and Xuanhan Wang and Jingkuan Song and Heng Tao Shen},
  year          = {2026},
  eprint        = {2607.03941},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url           = {https://arxiv.org/abs/2607.03941}
}

Acknowledgments

WSA builds on the open-source efforts of LeRobot, Qwen3-VL, Wan2.2, Cosmos Tokenizer, Depth Anything 3, RoboTwin, LIBERO, InternVLA-A1, and Fast-WAM. Please also follow the licenses and citation requirements of the corresponding projects.