# Paper proposes path to improve robot zero-shot generalization through data diversity

- Source: arXiv cs.RO
- Published: 2026-10-22T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/phex6ol5ywb19ozogqczdz2m5
- Original: https://arxiv.org/abs/2610.29829

## Summary

An arXiv paper formalizes zero-shot generalization in physical AI as coverage of the deployment distribution, noting that more samples from the same collector can only slowly shrink the unseen region, while each additional contributor can expand coverage. Based on this, the paper proposes a shared latent space for robot actions that combines multimodal alignment, generative augmentation, and noisy-label learning, and suggests that developers build applications around the standard robot Origmi to provide diverse data. It concludes with a testable hypothesis and evaluation protocol linking real-robot zero-shot success rates to data diversity.