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

TL;DR

FastUMI is a redesigned, hardware-independent manipulation interface that enables rapid, cost-effective data collection for robotic learning, supported by a large diverse dataset and simplified deployment pipeline.

Contribution

FastUMI introduces a hardware-agnostic, simplified manipulation interface with an ecosystem for data collection, verification, and integration, significantly improving scalability and ease of use.

Findings

FastUMI enables rapid deployment and reduces operational costs.

It maintains high accuracy with simplified hardware and algorithms.

The dataset includes over 10,000 diverse real-world demonstrations.

Abstract

Real-world manipulation data involving robotic arms is crucial for developing generalist action policies, yet such data remains scarce since existing data collection methods are hindered by high costs, hardware dependencies, and complex setup requirements. In this work, we introduce FastUMI, a substantial redesign of the Universal Manipulation Interface (UMI) system that addresses these challenges by enabling rapid deployment, simplifying hardware-software integration, and delivering robust performance in real-world data acquisition. Compared with UMI, FastUMI has several advantages: 1) It adopts a decoupled hardware design and incorporates extensive mechanical modifications, removing dependencies on specialized robotic components while preserving consistent observation perspectives. 2) It also refines the algorithmic pipeline by replacing complex Visual-Inertial Odometry (VIO) implementations with an off-the-shelf tracking module, significantly reducing deployment complexity while maintaining accuracy. 3) FastUMI includes an ecosystem for data collection, verification, and integration with both established and newly developed imitation learning algorithms, accelerating policy learning advancement. Additionally, we have open-sourced a high-quality dataset of over 10,000 real-world demonstration trajectories spanning 22 everyday tasks, forming one of the most diverse UMI-like datasets to date. Experimental results confirm that FastUMI facilitates rapid deployment, reduces operational costs and labor demands, and maintains robust performance across diverse manipulation scenarios, thereby advancing scalable data-driven robotic learning.