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Generalizable Robotic Insertion with World Models

Paper recorded by Signals 4 on 2026-09-23 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-23 on arXiv · recorded by Signals 4 on 2026-09-24

Category: cs.CV · 计算机视觉 · first seen 2026-09-24

Abstract

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

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#14 most recent of 301 cs.CV papers we have recorded · ↑ newer: RAMP: Robust Adaptive Mixed-Precision Quantization for Edge CPU Vision · ↓ older: ODPure: Backdoor Purification for Object Detection via Ensemble Corrup
Cite this page: Generalizable Robotic Insertion with World Models: the #14 most recent of 301 cs.CV papers we have recorded (as of 2026-09-23). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/generalizable-robotic-insertion-with-world-models.html
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