Generalizable Robotic Insertion with World Models
Robotic assembly in high-mixture settings requires adaptable systems that can handle diverse parts, yet current approaches typically rely on policies specialized to each insertion task. Although this can reach high success rates, it makes the process of deploying systems for new problems tedious and time consuming. We present a framework for generalizable insertion using world models that combine robot proprioceptive information with raw visual observations captured by a wrist-mounted camera. Our model-based approach trains a single world model on up to 90 insertion tasks with geometrically diverse parts, achieving 56% zero-shot success on unseen objects with unknown geometry compared to ...
| Authors | Nicklas Hansen, Iretiayo Akinola, Yijie Guo et al. |
|---|---|
| Primary category | cs.RO |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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