AIRSPEED: An Open Source Data Production Platform for Embodied Artificial Intelligence

Read full text
Authors: Xuan Xia, Xianqiao Tong, Yu Bo, Jialin Jiao, Xinmin Ding, Xing He, hongjun zhou, Haoran Tong, yufei lin, tongyi shen, Ning Ding, Shaoshan Liu
Cited by: 1
References: 4

Abstract

The development of embodied AI (EAI) critically depends on efficient data acquisition, yet faces persistent challenges including high costs, limited training scenarios, and lack of standardized datasets. We present AIRSPEED, an open source data production platform designed to address these bottlenecks through three core innovations. First, AIRSPEED achieves hardware–software decoupling via unified robot and simulation interfaces, enabling seamless integration with diverse data collection devices and simulation platforms. Second, it supports comprehensive data production methods spanning teleoperation and teaching approaches, as well as synthetic data generation through data synthesis and virtual teleoperation. Third, AIRSPEED automates pyramid-structured dataset construction compatible with both HDF5 and LeRobot formats, significantly reducing manual overhead. Experimental validation demonstrates substantial efficiency gains, achieving up to 35.6× acceleration in dataset construction and 6.0× overall speedup compared to manual workflows. With end-to-end latency as low as 3 ms and compression throughput exceeding 296 MB/s, AIRSPEED establishes a scalable foundation for EAI data production. AISPEED is open sourced on this website: URL .

Fields of Study & Subjects

Human Motion and Animation Social Robot Interaction and HRI Robot Manipulation and Learning

Publication details

Journal / source
ACM Transactions on Cyber-Physical Systems
Publisher
Association for Computing Machinery (ACM)
Volume
10
Issue
3
Pages
1–22
Publication date
8 April 2026
Language
EN