arXiv Machine Learning September 24, 2026 By Nicklas Hansen, Iretiayo Akinola, Yijie Guo, Jie Xu, Bingjie Tang, Hao Su, Xiaolong Wang, Abhishek Gupta, Dieter Fox, Yashraj Narang

Generalizable Robotic Insertion with World Models

Read the original on arXiv Machine Learning →

The paper introduces a framework that uses world models to enable robotic insertion across diverse parts. By combining proprioceptive data with wrist‑mounted camera visuals, a single model is trained on up to 90 tasks, achieving 56% zero‑shot success on unseen objects versus 7% for a model‑free baseline. The approach scales with more training objects and can be fine‑tuned for improved data efficiency and performance.

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 Machine Learning.

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