PVI: Plug-in Visual Injection for Vision-Language-Action Models
This work addresses a bottleneck in vision-language-action models for robotics by enhancing temporal information without major architectural changes, though it is incremental as it builds on existing injection strategies.
The paper tackled the problem of vision-language-action models attenuating fine-grained geometric and temporal cues by proposing Plug-in Visual Injection (PVI), a lightweight module that injects auxiliary visual features, resulting in consistent gains over base policies and improved performance on multi-phase tasks requiring state tracking.
VLA architectures that pair a pretrained VLM with a flow-matching action expert have emerged as a strong paradigm for language-conditioned manipulation. Yet the VLM, optimized for semantic abstraction and typically conditioned on static visual observations, tends to attenuate fine-grained geometric cues and often lacks explicit temporal evidence for the action expert. Prior work mitigates this by injecting auxiliary visual features, but existing approaches either focus on static spatial representations or require substantial architectural modifications to accommodate temporal inputs, leaving temporal information underexplored. We propose Plug-in Visual Injection (PVI), a lightweight, encoder-agnostic module that attaches to a pretrained action expert and injects auxiliary visual representations via zero-initialized residual pathways, preserving pretrained behavior with only single-stage fine-tuning. Using PVI, we obtain consistent gains over the base policy and a range of competitive alternative injection strategies, and our controlled study shows that temporal video features (V-JEPA2) outperform strong static image features (DINOv2), with the largest gains on multi-phase tasks requiring state tracking and coordination. Real-robot experiments on long-horizon bimanual cloth folding further demonstrate the practicality of PVI beyond simulation.