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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2508.06547 |
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| _version_ | 1866916889677004800 |
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| author | Wu, Heran Zhou, Zirun Zhang, Jingfeng |
| author_facet | Wu, Heran Zhou, Zirun Zhang, Jingfeng |
| contents | Traditional robotic systems typically decompose intelligence into independent modules for computer vision, natural language processing, and motion control. Vision-Language-Action (VLA) models fundamentally transform this approach by employing a single neural network that can simultaneously process visual observations, understand human instructions, and directly output robot actions -- all within a unified framework. However, these systems are highly dependent on high-quality training datasets that can capture the complex relationships between visual observations, language instructions, and robotic actions. This tutorial reviews three representative systems: the PyBullet simulation framework for flexible customized data generation, the LIBERO benchmark suite for standardized task definition and evaluation, and the RT-X dataset collection for large-scale multi-robot data acquisition. We demonstrated dataset generation approaches in PyBullet simulation and customized data collection within LIBERO, and provide an overview of the characteristics and roles of the RT-X dataset for large-scale multi-robot data acquisition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06547 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A tutorial note on collecting simulated data for vision-language-action models Wu, Heran Zhou, Zirun Zhang, Jingfeng Robotics Traditional robotic systems typically decompose intelligence into independent modules for computer vision, natural language processing, and motion control. Vision-Language-Action (VLA) models fundamentally transform this approach by employing a single neural network that can simultaneously process visual observations, understand human instructions, and directly output robot actions -- all within a unified framework. However, these systems are highly dependent on high-quality training datasets that can capture the complex relationships between visual observations, language instructions, and robotic actions. This tutorial reviews three representative systems: the PyBullet simulation framework for flexible customized data generation, the LIBERO benchmark suite for standardized task definition and evaluation, and the RT-X dataset collection for large-scale multi-robot data acquisition. We demonstrated dataset generation approaches in PyBullet simulation and customized data collection within LIBERO, and provide an overview of the characteristics and roles of the RT-X dataset for large-scale multi-robot data acquisition. |
| title | A tutorial note on collecting simulated data for vision-language-action models |
| topic | Robotics |
| url | https://arxiv.org/abs/2508.06547 |