---
title: "Embodied AI Races for Data: RealMan Deploys Robots in Real-World Scenarios"
type: "News"
locale: "en"
url: "https://longbridge.com/en/news/296411345.md"
description: "Embodied AI is hitting a \"data wall.\" In recent years, the rapid improvement in large model capabilities has largely been built on the vast amounts of text,"
datetime: "2026-08-19T23:47:23.000Z"
locales:
  - [zh-CN](https://longbridge.com/zh-CN/news/296411345.md)
  - [en](https://longbridge.com/en/news/296411345.md)
  - [zh-HK](https://longbridge.com/zh-HK/news/296411345.md)
---

# Embodied AI Races for Data: RealMan Deploys Robots in Real-World Scenarios

Embodied AI is hitting a "data wall."

In recent years, the rapid improvement in large model capabilities has largely been built on the vast amounts of text, images, and videos available on the internet. However, what embodied AI requires is data derived from interactions within the physical world.

Collecting such data is evidently not easy, and this has become a practical bottleneck hindering the further advancement of embodied AI.

Consequently, topics surrounding data acquisition have garnered widespread attention.

At the 2026 World Robot Conference (WRC), data acquisition has become a key showcase for many embodied AI companies.

RealMan Intelligent Technology (Beijing) Co., Ltd. (hereinafter referred to as "RealMan") is one of them. It has deployed its RealBOT robots in scenarios such as pharmacies, power distribution rooms, and food preparation areas: the robots handle restocking and medication retrieval in smart pharmacies, perform inspections in power distribution rooms, and collaborate with masters at Beijing Daoxiangcun via teleoperation to make mooncakes.

At this WRC, remote operators in Beijing could control robots in the RealMan exhibition hall to make mooncakes, while also remotely operating robots at the Changzhou factory in real-time to complete equipment debugging and production line inspections.

From the perspective of model training, each instance of human takeover essentially constitutes a real-robot trajectory generated by human demonstration. The operator decides the next step, the robot moves according to the instructions, and cameras, joints, end-effectors, and other sensors synchronously record the entire process. Compared to performing pre-designed actions only in fixed training environments, these data come directly from ongoing, real-world work.

According to Wall Street News, RealMan plans to deploy its RealBOT series robots to nearly 1,000 real-world scenarios in 2026 through its GLN Remote Operation Network, continuously collecting real-robot data during actual operations.

In the current context of scarce real-robot data, this strategy is expected to bring greater growth potential to RealMan.

Regarding its capital market progress, RealMan initiated IPO counseling on August 10 this year, with Guotai Haitong serving as the counseling institution, aiming to complete the counseling within the year.

Whether RealMan can successfully knock on the door of the A-share market is drawing significant attention.

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