VLA-Forget: Vision-Language-Action Unlearning for Embodied Foundation Models
Vision-language-action (VLA) models are emerging as embodied foundation models for robotic manipulation, but their deployment introduces a new unlearning challenge: removing unsafe, spurious, or privacy-sensitive behaviors without degrading perception, language grounding, and action control. In OpenVLA-style policies, behavior is produced through a fused visual encoder, a cross-modal projector, and a language backbone that predicts tokenized robot actions, so undesirable knowledge can be distributed across perception, alignment, and reasoning/action layers rather than confined to a single module. Consequently, partial unlearning applied only to the vision stack or only to the language bac...
| Authors | Ravi Ranjan, Agoritsa Polyzou |
|---|---|
| Primary category | cs.CV |
| Published | 5 April 2026 |
| Last updated at source | 22 April 2026 |
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