FastUMI: A Scalable and Hardware-Independent Universal Manipulation Interface with Dataset

Zhaxizhuoma, Kehui Liu, Chuyue Guan, Zhongjie Jia, Ziniu Wu, Xin Liu, Tianyu Wang, Shuai Liang, Pengan CHEN, Pingrui Zhang, Haoming Song, Delin Qu, Dong Wang, Zhigang Wang, Nieqing Cao, Yan Ding, Bin Zhao, Xuelong Li

Keywords: Imitation Learning, Manipulation, Data Collection

Abstract:

Real-world manipulation datasets for robotic arms remain scarce due to the high costs, rigid hardware dependencies, and complex setup procedures associated with existing data collection methods.
We introduce, a redesigned Universal Manipulation Interface (UMI) that addresses these challenges, enabling low-cost, scalable, and rapid deployment across heterogeneous platforms.
FastUMI achieves this through: (i) hardware decoupling via extensive mechanical reengineering, which removes dependence on specialized robotic components while preserving a consistent visual perspective; (ii) replacement of complex visual–inertial odometry with a commercial off-the-shelf tracker, simplifying the software stack without compromising pose estimation accuracy; and (iii) the provision of an integrated ecosystem that streamlines data acquisition, automates quality control, and ensures compatibility with both standard and enhanced imitation-learning pipelines.
To facilitate further research, we release an open-access dataset comprising over 15,000 real-world demonstrations spanning 24 tasks constituting one of the most extensive UMI-like resources to date.
Empirical evaluations show that FastUMI supports rapid deployment, reduces operational overhead, and delivers robust performance across diverse manipulation scenarios, advancing scalable data-driven robotic learning.

Submission Number: 249