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arXiv AI By Rafael Felix, Flavia Yanase

Robot Generalization Beyond Volume: The Diversity Path via Zero-Shot Capabilities

Read the original on arXiv AI →

The paper argues that zero‑shot generalization in robotics is limited by a lack of diverse training data, unlike digital AI where crowd‑sourced data drives progress. It defines zero‑shot generalization as coverage of the deployment distribution and shows that adding more samples from the same source only marginally reduces the unseen region, whereas new contributors significantly expand it. The authors propose a shared latent space for robot actions, combining multi‑modal alignment, generative augmentation, and noisy‑label learning, and suggest building a community of developers around a single standard robot, Origami, to generate diverse data for testing zero‑shot success.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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