Fast-UMI: A Scalable and Hardware-Independent Universal Manipulation Interface
<|reference_start|>Fast-UMI: A Scalable and Hardware-Independent Universal Manipulation Interface: Collecting real-world manipulation trajectory data involving robotic arms is essential for developing general-purpose action policies in robotic manipulation, yet such data remains scarce. Existing methods face limitations such as high costs, labor intensity, hardware dependencies, and complex setup requirements involving SLAM algorithms. In this work, we introduce Fast-UMI, an interface-mediated manipulation system comprising two key components: a handheld device operated by humans for data collection and a robot-mounted device used during policy inference. Our approach employs a decoupled design compatible with a wide range of grippers while maintaining consistent observation perspectives, allowing models trained on handheld-collected data to be directly applied to real robots. By directly obtaining the end-effector pose using existing commercial hardware products, we eliminate the need for complex SLAM deployment and calibration, streamlining data processing. Fast-UMI provides supporting software tools for efficient robot learning data collection and conversion, facilitating rapid, plug-and-play functionality. This system offers an efficient and user-friendly tool for robotic learning data acquisition.<|reference_end|>
@article{wu2024fast-umi:,
    title={Fast-UMI: A Scalable and Hardware-Independent Universal Manipulation
  Interface},
    author={Ziniu Wu, Tianyu Wang, Zhaxizhuoma, Chuyue Guan, Zhongjie Jia, Shuai
  Liang, Haoming Song, Delin Qu, Dong Wang, Zhigang Wang, Nieqing Cao, Yan
  Ding, Bin Zhao, Xuelong Li},
    journal={arXiv preprint arXiv:2409.19499},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19499},
    primaryClass={cs.RO}
}