外观
论文arXiv 2610.1245120 天前
VersaCamVLA：解耦相机配置实现变视角鲁棒操作
本文由 AI 根据公开内容整理，可能有误，请以原文为准：VersaCamVLA: Camera-Configurable VLA Policies for Robotic Manipulation（arXiv 2610.12451）
核心内容
针对视觉-语言-动作（VLA）模型在部署时因相机数量或位姿变化导致性能下降的问题，研究团队提出VersaCamVLA框架。该方法将相机集表示与动作学习解耦，通过学习统一场景Token接口，将任意可变数量的带位姿RGB视图映射为固定大小潜在场景Token。实验覆盖RoboTwin、LIBERO仿真基准及真实机器人平台，结果显示其在不同相机配置下均优于传统VLA及直接多视图基线方法。
技术核心在于利用多信号目标视图预测与腕部增强位姿采样（WAPS），借助自然腕部相机运动获取免费位姿多样性，避免额外数据采集成本。部署阶段仅需轻量级空间编码器将紧凑场景Token注入预训练基础VLA作为补充视觉条件，无需显式3D感知或新视角渲染。该架构使模型能在未见过的相机位姿和数量变化中保持鲁棒性，显著降低具身智能从仿真到真机的视觉适配门槛。
关键看点
- 统一场景Token：将任意数量带位姿RGB视图映射为固定尺寸潜在Token，解耦相机配置与动作策略学习
- WAPS采样机制：利用腕部相机自然运动生成多样化位姿数据，零成本扩充训练集视角分布
- 轻量化部署：通过空间编码器注入场景Token至预训练VLA，无需3D传感器或新视角渲染模块
- 跨域鲁棒验证：在RoboTwin、LIBERO及真机平台测试，变相机数量与未见位姿下性能持续优于基线
工程与产业观察
该工作为VLA模型在非结构化环境中的视觉泛化提供工程范式，通过Token化解耦设计消除对固定相机标定的强依赖。WAPS机制利用本体运动挖掘数据价值，降低了多视角采集成本。对于需在动态产线或多机型间迁移操作策略的团队，此方案提供了无需重构感知栈的即插即用适配路径。
英文摘要 (Abstract)
Vision-Language-Action (VLA) models have emerged as powerful foundations for robotic manipulation, but their reliance on fixed camera configurations during training makes them brittle to changes in camera count or pose during deployment. To overcome these limitations, we propose VersaCamVLA, a camera-configurable framework that decouples camera-set representation from action learning. VersaCamVLA learns a unified scene-token interface that maps an arbitrary, variable set of posed RGB views into fixed-size latent scene tokens. This is achieved via multi-signal target-view prediction and Wrist-Augmented Pose Sampling (WAPS), which leverages natural wrist-camera motion for free pose diversity. At deployment, a lightweight spatial encoder injects these compact scene tokens into a pretrained base VLA as a supplementary visual condition, requiring no explicit 3D sensing or novel-view rendering. Experiments on RoboTwin, LIBERO, and a real-robot platform demonstrate that VersaCamVLA consistently outperforms prior VLA methods and direct multi-view baselines, maintaining robust performance across varying camera counts and unseen camera poses.
作者：Boyao Han, Chen Shi, Jingjing Qian, ZhuoTan Tian, Li Jiang
来源
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