MobileVISTA: Generative Data Augmentation for Pose Generalization in Mobile Manipulation
Mobile manipulators such as humanoid robots are increasingly deployed in dynamic, unstructured environments to perform dexterous manipulation tasks. However, end-to-end manipulation policies trained to imitate demonstration data collected from a single robot pose are brittle: even centimeter-scale deviations in robot pose at deployment can drive ego-centric observations and end-effector trajectories out of the training distribution, leading to sharp drops in performance. We introduce MobileVISTA, a data generation framework that transforms demonstrations captured at canonical poses into diverse, pose-perturbed training data by jointly (1) augmenting egocentric visual observations and (2) ...
| Authors | Suzannah Wistreich, Stephen Tian, Isabella Huang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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