RIO: Flexible Real-Time Robot I/O for Cross-Embodiment Robot Learning
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Ortega-Kral, Pablo Xing, Eliot Bucker, Arthur Luk, Vernon Kim, Junseo Kwon, Owen Xie, Angchen Sobanbabu, Nikhil Yuan, Yifu Lee, Megan Ameria, Deepam Ayapilla, Bhaswanth Bussell, Jaycie Shi, Guanya Francis, Jonathan Oh, Jean |
| author_facet | Ortega-Kral, Pablo Xing, Eliot Bucker, Arthur Luk, Vernon Kim, Junseo Kwon, Owen Xie, Angchen Sobanbabu, Nikhil Yuan, Yifu Lee, Megan Ameria, Deepam Ayapilla, Bhaswanth Bussell, Jaycie Shi, Guanya Francis, Jonathan Oh, Jean |
| contents | Despite recent efforts to collect multi-task, multi-embodiment datasets, to design recipes for training Vision-Language-Action models (VLAs), and to showcase these models on different robot platforms, generalist cross-embodiment robot capabilities remains a largely elusive ideal. Progress is limited by fragmented infrastructure: most robot code is highly specific to the exact setup the user decided on, which adds major overhead when attempting to reuse, recycle, or share artifacts between users. We present RIO (Robot I/O), an open source Python framework that provides flexible, lightweight components for robot control, teleoperation, data formatting, sensor configuration, and policy deployment across diverse hardware platforms and morphologies. RIO provides abstractions that enable users to make any choice and to switch between them, with minimal reconfiguration effort. We validate RIO on VLA deployment workflows across three morphologies (single-arm, bimanual, humanoid) and four hardware platforms with varying grippers and cameras. Using teleoperated data collected with RIO, we fine-tune state-of-the-art VLAs including $π_{0.5}$ and GR00T on household tasks such as pick-and-place, folding, and bowl scrubbing. By open sourcing all our efforts, we hope the community can accelerate their pace of robot learning on real-world robot hardware. Additional details at: https://robot-i-o.github.io |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_11564 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RIO: Flexible Real-Time Robot I/O for Cross-Embodiment Robot Learning Ortega-Kral, Pablo Xing, Eliot Bucker, Arthur Luk, Vernon Kim, Junseo Kwon, Owen Xie, Angchen Sobanbabu, Nikhil Yuan, Yifu Lee, Megan Ameria, Deepam Ayapilla, Bhaswanth Bussell, Jaycie Shi, Guanya Francis, Jonathan Oh, Jean Robotics Despite recent efforts to collect multi-task, multi-embodiment datasets, to design recipes for training Vision-Language-Action models (VLAs), and to showcase these models on different robot platforms, generalist cross-embodiment robot capabilities remains a largely elusive ideal. Progress is limited by fragmented infrastructure: most robot code is highly specific to the exact setup the user decided on, which adds major overhead when attempting to reuse, recycle, or share artifacts between users. We present RIO (Robot I/O), an open source Python framework that provides flexible, lightweight components for robot control, teleoperation, data formatting, sensor configuration, and policy deployment across diverse hardware platforms and morphologies. RIO provides abstractions that enable users to make any choice and to switch between them, with minimal reconfiguration effort. We validate RIO on VLA deployment workflows across three morphologies (single-arm, bimanual, humanoid) and four hardware platforms with varying grippers and cameras. Using teleoperated data collected with RIO, we fine-tune state-of-the-art VLAs including $π_{0.5}$ and GR00T on household tasks such as pick-and-place, folding, and bowl scrubbing. By open sourcing all our efforts, we hope the community can accelerate their pace of robot learning on real-world robot hardware. Additional details at: https://robot-i-o.github.io |
| title | RIO: Flexible Real-Time Robot I/O for Cross-Embodiment Robot Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.11564 |