Robotic Policy Adaptation via Weight-Space Meta-Learning
Vision-Language-Action (VLA) models are emerging as a promising paradigm for robotic manipulation, enabling general-purpose policies trained from large corpora of demonstrations and action labels. However, adapting these models to new tasks still typically requires task-specific demonstrations, action annotations, and additional fine-tuning, making deployment costly and difficult to scale. We propose WIZARD, a weight-space meta-learning framework that sidesteps task-specific fine-tuning by generating task-specific LoRA parameters for a frozen VLA policy. Given only a language instruction and a short demonstration video, WIZARD predicts the corresponding adaptation weights in a single forw...
| Authors | Christian Bianchi, Siamak Yousefi, Alessio Sampieri et al. |
|---|---|
| Primary category | cs.RO |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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