Difference between revisions of "Universal Manipulation Interface"
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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 | + | {{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 | + | | 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: | ||
| + | |||
| + | # 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]] | ||
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 | |
|---|---|
| Name | UMI |
| Full Name | Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots |
| Arxiv | Link |
| Publication Date | February 2024 |
| Authors | Cheng Chi, Zhenjia Xu, 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
