Difference between revisions of "Universal Manipulation Interface"

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

Latest revision as of 00:18, 1 May 2024

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.

UMI
NameUMI
Full NameUniversal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots
ArxivLink
TwitterTwitter
Publication DateFebruary 2024
AuthorsCheng Chi, Zhenjia Xu, Chuer Pan, Eric Cousineau, Benjamin Burchfiel, Siyuan Feng, Russ Tedrake, Shuran Song

The UMI paper was novel for several reasons:

  1. It completely avoids robot teleoperation and the associated latency. This lets the robot do things like reliably tossing balls.
  2. It provides a low-cost, scalable way to collect lots of data in the wild