The development of embodied artificial intelligence (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 3ms and compression throughput exceeding 296MB/s, AIRSPEED establishes a scalable foundation for EAI data production. AISPEED is open-sourced on this website: URL .
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xuan xia
Xianqiao Tong
Bo Yu
ACM Transactions on Cyber-Physical Systems
IDEX Corporation (United States)
Shenzhen Academy of Robotics
Mirai Hospital
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xia et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69d895796c1944d70ce06846 — DOI: https://doi.org/10.1145/3806827