IO-AI Tech, April 2026 Update: Industry Recognition, Ecosystem Participation, and International Exchange
Embodied AI has moved beyond research demonstrations into production deployments over the past 18 months, with humanoids, dexterous manipulators, and high-DoF robot arms entering logistics, manufacturing, and service environments at a pace the broader infrastructure stack has only recently begun to support. However, the data layer that underpins these deployments remains the most common bottleneck, fragmented across collection methods, inconsistent in quality, and rarely connected to the training pipelines that determine how quickly real-world performance improves. Industry conferences across April returned repeatedly to a shared observation, that physical AI cannot scale without standardized, interoperable, real-world data infrastructure operating at the same cadence as the robotic deployments it serves.
IO-AI Tech occupies the data infrastructure layer for physical AI, providing real-world data collection, teleoperation platforms, and closed-loop pipelines that connect human operators to robot fleets and feed continuous model improvement. April 2026 brought five developments along that thesis, spanning industry recognition, ecosystem participation, technical demonstration, and international academic exchange.
Recognised As "Top 60 Most Investment-Worthy Robotics Companies of 2026"
The 3rd China Robotics Industry Conference and Embodied AI 50 Forum took place in Shenzhen on April 10 and 11, drawing leadership from across the robotics value chain. At the conference's Golden Flint Award ceremony, IO-AI Tech was named one of the Top 60 Most Investment-Worthy Companies in the 2026 Robotics Industry, a recognition of IO-AI Tech's leadership in the embodied AI data layer, including technical depth, customer traction, and path to commercial scale. Co-founder Zhezhang Ding was invited to participate in the conference globalization roundtable, addressing the operational requirements for global data infrastructure and the patterns by which cross-border scenario partnerships take shape in practice.
Joining the Hive Data Co-Creation Initiative
At the launch event for Maniformer, a one-stop physical AI data service platform held on April 16, IO-AI Tech formally joined the "Hive Data Co-Creation Initiative" as a core builder of the global physical AI data ecosystem. The initiative brings together Maniformer Technology, JD Cloud, Baidu Cloud, Alibaba Cloud, and other infrastructure partners with the shared objective of standardization, scale, and interoperability across physical AI data. IO-AI Tech's contribution centers on three capabilities the embodied AI data layer requires at production scale, including high-precision teleoperation data collection, real-world human data collection, and embodied data management.
Motion Capture Symposium and Industry-Academia Signing in Sanya
The Sanya Embodied AI Robotics Innovation Center hosted "Smart-Connected Virtual and Real: Pioneering the Future" on April 23, combining a motion capture technology symposium with an industry-academia-research cooperation signing ceremony. The event, organized by Changzhi Embodied Intelligence (Hainan) Technology Co., Ltd., brought together government, industrial, and academic representatives to address the integration of motion capture into embodied AI and humanoid robotics deployment. IO-AI Tech co-founder Fulong Yin delivered the technical session on motion-capture-to-teleoperation adaptation, high-precision data collection, and how that data feeds robot training pipelines. The team also ran live teleoperation demonstrations alongside the innovation center's technical group, including the Lingxi X2 "one-to-two" configuration in which a single operator runs two robots concurrently.
Showcasing End-to-End Embodied AI Data Solutions at FAIR plus 2026
FAIR plus 2026 was held from April 22 to 24 at the Shenzhen Convention and Exhibition Center, expanding to 15,000 square meters with more than 500 industry chain companies and 200 industry leaders. IO-AI Tech presented its end-to-end embodied AI data pipeline across three live demonstrations, drawing interest from industry partners, research institutions, and global customers.
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Unitree G1 with remote and real-time full-body teleoperation: An operator wearing full-body motion capture equipment had their movement mapped in real time to a Unitree G1 humanoid for fine-grained grasping and obstacle-avoidance walking, illustrating the operator-to-robot motion fidelity that turns teleoperation sessions into training-grade data.
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One exoskeleton glove controlling ten dexterous hands across manufacturers: A single operator wearing one exoskeleton glove simultaneously controlled ten dexterous hands from different manufacturers, demonstrating TeleXperience's adaptability across robot configurations alongside IO-AI Tech's accumulated work on multi-source heterogeneous data fusion.
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SenseXperience real-world human data collection: Visitors stepped through the SenseXperience capture flow themselves, with first-person view, wrist view, gripper interaction, and post-processing turning their natural movements into structured multimodal training data with real-time visible processing.
Singapore Institute of Technology Site Visit
IO-AI Tech headquarters hosted a delegation from Singapore Institute of Technology on April 23 for a site visit and a working discussion of the embodied AI data pipeline. The conversation focused on four areas, including international cooperation, joint talent development, industry-academia-research coordination, and the buildout of an embodied AI base in Singapore. Singapore Institute of Technology is an applied university with strengths in technology development, industrial project translation, and engineering talent, an orientation that aligns directly with IO-AI Tech's work on data infrastructure, industrial deployment, and embodied AI applications.
Partner with IO-AI Tech
Building the data infrastructure for physical AI remains a key challenge for the embodied AI community. Working with IO-AI Tech on real-world data collection, teleoperation, and closed-loop training pipelines, robotics teams, infrastructure partners, and applied research organizations can move embodied AI deployments toward production scale, ensuring that real-world data, model improvement, and operator oversight reinforce one another as deployments expand.