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

2024-09-29

AI Summary

Real-world robotic manipulation data is scarce due to high collection costs, reliance on specialized hardware, and complex deployment. Method: We propose FastUMI—a hardware-agnostic, rapidly deployable universal manipulation interface. Its core innovations include a decoupled hardware architecture and plug-and-play commercial visual-inertial tracking modules that replace conventional VIO systems, coupled with a lightweight action-observation alignment algorithm and an end-to-end data collection–validation–learning framework. Contribution/Results: We release an open-source, high-quality real-world dataset comprising >10,000 trajectories across 22 everyday manipulation tasks. Empirical evaluation shows a 3× speedup in deployment time, a 50% reduction in hardware cost, and strong generalization and robust manipulation performance across diverse robotic platforms and environments.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Motion & Tracking
• Humans and AI: Interaction Techniques and Devices

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Mechanical Arm Data Collection
• High Cost
• Special Equipment Requirement

Innovation

• Flexible Design
• Off-the-shelf Tracking Module
• Comprehensive Data Collection System