Robotics policy keeps the vision backbone frozen and trains on a consumer GPU
mayfer · x · 2026-07-25
A robotics policy recipe that freezes a strong vision backbone, feeds all patch tokens into a small transformer policy, and uses a block-causal mask.
- No VLM or backbone fine-tuning is needed.
- The approach works with transformer-based policies such as VQ-BeT and Diffusion Policy.
- It is reported to train on a consumer GPU.
The attached diagram shows an observation trunk that encodes multi-view observations and goal inputs, then a policy head with frame-wise attention producing actions over time.
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