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Computer Vision Papers·2026
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World In-Context Learning for Agentic Robot Manipulation

Heng Fang, Zehui Guo +4·Huazhong University of Science and Technology, ArcheOne AI

Lets a frozen model learn robot manipulation from one demonstration by rehearsing in a simulated world and storing feedback in context rather than weights. It reports 83.4% mean success across 50 RoboTwin 2.0 tasks.

#paper#robotics#embodied-ai#manipulation#foundation-model+2
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