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'''Universal Manipulation Interface''' is a data collection and policy learning framework that allows direct skill transfer from in-the-wild human demonstrations to deployable robot policies.
{{infoboxpaper
| name = UMI
| full_name = Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots
| twitter_link = https://twitter.com/chichengcc/status/1758539728444629158
| date = February 2024
| authors = Cheng Chi, Zhenjia xuXu, Chuer Pan, Eric Cousineau, Benjamin Burchfiel, Siyuan Feng, Russ Tedrake, Shuran Song
}}
The UMI paper was novel for several reasons:
# It completely avoids robot teleoperation and the associated latency. This lets the robot do things like reliably tossing balls.
# It provides a low-cost, scalable way to collect lots of data in the wild
[[Category: Papers]]