Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916148414513152 |
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| author | Chi, Cheng Xu, Zhenjia Pan, Chuer Cousineau, Eric Burchfiel, Benjamin Feng, Siyuan Tedrake, Russ Song, Shuran |
| author_facet | Chi, Cheng Xu, Zhenjia Pan, Chuer Cousineau, Eric Burchfiel, Benjamin Feng, Siyuan Tedrake, Russ Song, Shuran |
| contents | We present Universal Manipulation Interface (UMI) -- a data collection and policy learning framework that allows direct skill transfer from in-the-wild human demonstrations to deployable robot policies. UMI employs hand-held grippers coupled with careful interface design to enable portable, low-cost, and information-rich data collection for challenging bimanual and dynamic manipulation demonstrations. To facilitate deployable policy learning, UMI incorporates a carefully designed policy interface with inference-time latency matching and a relative-trajectory action representation. The resulting learned policies are hardware-agnostic and deployable across multiple robot platforms. Equipped with these features, UMI framework unlocks new robot manipulation capabilities, allowing zero-shot generalizable dynamic, bimanual, precise, and long-horizon behaviors, by only changing the training data for each task. We demonstrate UMI's versatility and efficacy with comprehensive real-world experiments, where policies learned via UMI zero-shot generalize to novel environments and objects when trained on diverse human demonstrations. UMI's hardware and software system is open-sourced at https://umi-gripper.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10329 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots Chi, Cheng Xu, Zhenjia Pan, Chuer Cousineau, Eric Burchfiel, Benjamin Feng, Siyuan Tedrake, Russ Song, Shuran Robotics We present Universal Manipulation Interface (UMI) -- a data collection and policy learning framework that allows direct skill transfer from in-the-wild human demonstrations to deployable robot policies. UMI employs hand-held grippers coupled with careful interface design to enable portable, low-cost, and information-rich data collection for challenging bimanual and dynamic manipulation demonstrations. To facilitate deployable policy learning, UMI incorporates a carefully designed policy interface with inference-time latency matching and a relative-trajectory action representation. The resulting learned policies are hardware-agnostic and deployable across multiple robot platforms. Equipped with these features, UMI framework unlocks new robot manipulation capabilities, allowing zero-shot generalizable dynamic, bimanual, precise, and long-horizon behaviors, by only changing the training data for each task. We demonstrate UMI's versatility and efficacy with comprehensive real-world experiments, where policies learned via UMI zero-shot generalize to novel environments and objects when trained on diverse human demonstrations. UMI's hardware and software system is open-sourced at https://umi-gripper.github.io. |
| title | Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots |
| topic | Robotics |
| url | https://arxiv.org/abs/2402.10329 |