VersaCamVLA: Camera-Configurable VLA Policies for Robotic Manipulation

Summary

Abstract Summary

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.

Key Points

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.

Methodology

We present VersaCamVLA , a camera-configurable VLA framework that decouples camera-set representation learning from action learning, as illustrated in Fig. 2 . In Stage I, VersaCamVLA learns a unified scene-token interface between raw observations and the policy.

A scene encoder maps any non-empty set of posed RGB views to fixed-size latent scene tokens, supervised by target-view prediction, which encourages the scene tokens to preserve view-consistent spatial information across camera poses.

Methodology — VersaCamVLA: Camera-Configurable VLA Policies for Robotic Manipulation — Figure 1: Left: Different manipulation tasks prefer different camera views, making a fixed view setup suboptimal. Right: VersaCamVLA augments a vanilla VLA with a camera-configurable scene-token interface that enables flexible view setup at deployment.

Experiments

Simulation Setup. We evaluate on two standard robotic manipulation benchmarks, RoboTwin 2.0 [ 24 ] for bimanual manipulation under canonical ( Clean ) and domain-randomized ( DR ) settings, and LIBERO [ 25 ] for single-arm manipulation across four suites. Embodiments, task selection, demonstration counts, and training protocols are described in Appendix A . Camera-Configuration Setup.

Beyond the official benchmark settings, we design additional evaluations to test robustness to changing camera configurations. Camera-Pose Perturbation.

Experiments — VersaCamVLA: Camera-Configurable VLA Policies for Robotic Manipulation — Figure 2: Overview of VersaCamVLA. Stage I: A scene encoder aggregates posed RGB views into fixed-size scene tokens, with a training-only decoder predicting RGB, semantic, and edge signals at target views. Stage II: The decoder is discarded, the scene encoder is frozen, and a jointly-trained spatial encoder compresses the tokens into a supplementary visual condition for a pretrained VLA.

Sources and Demonstrations

Figures are reproduced from Han et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.