SimVLA: A Simple VLA Baseline for Robotic Manipulation
Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robotic manipulation, leveraging large-scale pre-training to achieve strong performance. The field has rapidly evolved with additional spatial priors and diverse architectural innovations. However, these advancements are often accompanied by varying training recipes and implementation details, which can make it challenging to disentangle the precise source of empirical gains. In this work, we introduce SimVLA, a streamlined baseline designed to establish a transparent reference point for VLA research. By strictly decoupling perception from control, using a standard vision-language backbone and a l...
| Authors | Yuankai Luo, Woping Chen, Tong Liang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 20 February 2026 |
| Last updated at source | 20 February 2026 |
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