WARP-VLA: Wrist-Camera Adaptation for View-Robust Policy Execution in Vision-Language-Action Models
To address this, we propose WARP-VLA, a camera-view robust VLA for diverse wrist camera configurations.
Key points
- Despite recent advances in Vision-Language-Action models (VLAs) for robotic manipulation, their performance remains sensitive to changes in camera configuration.
- Unlike fixed external views, wrist views are more challenging because the camera moves with the robot, causing even small mounting variations to alter fine-grained geometric cues.
- WARP-VLA adopts a Mixture-of-Experts (MoE) architecture where individual experts learn view-specific feature transformations, and a router combines them based on implicit view information.
- To facilitate reproducibility and future research, we release our wrist viewpoint robustness benchmark and a plug-and-play implementation.
Sources (1)
- [1]WARP-VLA: Wrist-Camera Adaptation for View-Robust Policy Execution in Vision-Language-Action ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 08:44 AM
To address this, we propose WARP-VLA, a camera-view robust VLA for diverse wrist camera configurations.
Despite recent advances in Vision-Language-Action models (VLAs) for robotic manipulation, their performance remains sensitive to changes in camera configuration.
Extractive summary: sentences quoted from the sources.
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