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VLA-Forget: Vision-Language-Action Unlearning for Embodied Foundation Models

Authors
Ravi Ranjan, Agoritsa Polyzou
Source
arXiv
Published
Collection
Embodied AI
Vision Language ActionRobot UnlearningEmbodied Foundation ModelsGeneralist Robot PolicySafety
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Summary

VLA-Forget targets selective unlearning for vision-language-action robot policies. It removes unsafe, spurious, or sensitive behaviors while trying to keep visual grounding, language conditioning, and action performance intact.

Problem

  • VLA robot policies can memorize bad behavior in several places at once: the visual encoder, the image-language connector, and the action-generating language model.
  • Standard unlearning methods for only vision models or only language models often miss part of the unwanted behavior or damage normal robot performance.
  • This matters because VLA errors become physical actions, such as following the wrong instruction while still producing a plausible grasp.

Approach

  • The method splits the model into three parts: visual encoder, cross-modal projector, and upper action/reasoning transformer layers.
  • It picks visual and projector layers to edit using a ratio-aware score based on forget gradients, retain gradients, parameter scale, and gradient conflict.
  • It picks action/reasoning layers using a significance ratio, starting with the most forget-relevant upper layers and adding more only if forgetting is still weak.
  • Training uses four losses together: retain loss on kept data, forget loss on target data, mismatch loss to push forgotten outputs away from the original model, and feature preservation loss on retained and boundary examples.
  • Updates are done in stages with LoRA adapters and PCGrad: first weaken visual triggers, then break bad visual-language bindings, then suppress residual action priors.

Results

  • In the abstract, the paper claims relative gains over strong baselines of 10% better forgetting efficacy, 22% better perceptual specificity, 9% better reasoning/task retention, and 55% lower post-quantization recovery.
  • On OpenVLA-7B / Open X-Embodiment, VLA-Forget reports FC 93, RC 91, FAD 0.88, RAD 0.21, TSR 78, SVR 5. Compared with NPO: RC improves 88 -> 91, TSR 74 -> 78, RAD drops 0.23 -> 0.21, SVR drops 8 -> 5. Compared with GA: same FC 93, but RC improves 60 -> 91 and TSR 40 -> 78.
  • On OpenVLA-7B / lerobot-pusht_image, VLA-Forget reports FC 95, RC 94, FAD 0.90, RAD 0.13, TSR 69, SVR 4. Compared with NPO: FC 92 -> 95, RC 90 -> 94, TSR 65 -> 69, RAD 0.15 -> 0.13, SVR 7 -> 4.
  • On pi0fast-base / Open X-Embodiment, VLA-Forget reports FC 94, RC 89, FAD 0.88, RAD 0.22, TSR 75, SVR 6. Compared with NPO: FC 89 -> 94, RC 87 -> 89, TSR 72 -> 75, RAD 0.24 -> 0.22. Compared with GA: FC 93 -> 94, RC 57 -> 89, TSR 38 -> 75, while SVR stays 6.
  • Across the shown tables, GA usually gets the strongest or tied forgetting score, but VLA-Forget keeps much higher retain utility and task success, which is the paper's main claimed advantage.

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