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DEXTEROUS MANIPULATION

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CordViP

CordViP research team

A correspondence-based policy implementation with data collection, training and real-robot evaluation code.

What you can explore

Inspect the object/hand geometry pipeline and how it feeds manipulation learning.

Before you use it

Uses separate pose-estimation weights, demo data and hardware integration. FoundationPose, policy components and upstream data have their own dependencies and terms.

Original contributors

Yankai Fu, Qiuxuan Feng, Ning Chen and collaborators

License / access: Code: MIT · check upstream models, data and dependency terms

Original code & documentation

Research overview

Editorial reference · checked 2026-09-22

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