Robust Bimanual Vision-Language-Action Models via Embarrassingly Simple Modality Masking
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Robots operating in real-world environments must execute complex, multi-step bimanual tasks over long horizons rather than single, isolated actions. Current manipulation datasets struggle to support t...
arXiv:2609.36416v1 Announce Type: cross Abstract: Robots operating in real-world environments must execute complex, multi-step bimanual tasks over long horizons rather than single, isolated actions....
arXiv:2603. 06001v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models enable robots to perform manipulation tasks directly from natural language instructions and are increasingly viewed as a foundation for generalist robotic policies.
arXiv:2609.36416v2 Announce Type: replace-cross Abstract: Robots operating in real-world environments must often execute complex, multi-step bimanual tasks over long horizons rather than single, isol...
The paper addresses shortcut learning in vision‑language‑action (VLA) models, showing that different backbones vary in their susceptibility to visual shortcuts that arise from limited demonstration diversity. It introduces a representation‑level metric called action margin to predict shortcut use without rollouts, and proposes task scrubbing—a domain‑adversarial training method—to reduce reliance on visual cues and enhance out‑of‑distribution robustness. Experiments in simulation and real‑world settings demonstrate that task scrubbing improves generalization and often eliminates visual shortcut learning.
Vision-Language-Action (VLA) models have emerged as a promising approach for generalizable robotic manipulations. In particular, flow matching-based VLA models have shown remarkable success due to their capability to generate precise and smooth action sequences and capture multimodal distributions.