Accelerating at the deployment stage, AgiBot aims to open up the full end-to-end pipeline of physical AI.

While the embodied intelligence industry is still keen on demonstrating technological progress through highly difficult movements, a different approach was presented at the Agibot Innovation Day. 

At this launch event, there were not many dazzling robot stunts, nor too many eye-catching group dances. Instead, it was more like a technical seminar with extremely high information density. Even at the entrance of the World Expo Center, Agibot set up a large "paper wall" to present the recent core technological progress of the Agibot team.

Over the past three years, robots have learned to run, jump, and do backflips, but after stepping out of the laboratory, people found that a single technological breakthrough cannot support stable work in complex environments. After large models such as GPT-6 Astra demonstrated physical task planning capabilities, "real deployment" has changed from a slogan to a question that the industry must answer.

Agibot's answer is a combination of measures:

The GO-3 Preview embodied foundational large model, the AGILE general motion intelligence algorithm framework, and the WITA-OMNI native multimodal interaction large model respectively tackle three major difficulties in operation, motion and interaction; Lingqu OS 1.0 and AHA3 third-generation hardware architecture unify the ports from both software and hardware ends; Worken (Valid Intelligent Working Hour) attempts to establish a measurement standard for the actual labor output of physical AI.

In other words, Agibot has almost laid out the entire technical chain, breaking through each bottleneck of models, data, hardware and software one by one.

All of Agibot's moves point to the same direction — The key to winning the next stage of competition in the robotics industry is no longer who is better at performing fancy movements, but who can implement the achievements of the past three years into real scenarios and turn them into continuous productivity.

The figures given by Peng Zhihui, Co-founder, President and CTO of Agibot at the launch event, exactly provide a footnote to this judgment: in the first half of 2026, the revenue of Agibot robots was close to 2 billion yuan, and the cumulative output of Agibot general AI robots reached 20,000 units.

In an interview after the meeting, he frankly said, "The production of 10,000 units is only a victory for the manufacturing industry, and the deployment of 10,000 units to work is the real victory for physical AI."

Embodied Deployment No Longer Relies on Single-Point Technology

In the past few years, a common competition method has emerged in the robotics industry: constantly refreshing the upper limit of a certain capability, such as running faster, jumping higher, and completing more complex grasping actions. However, when robots are actually deployed into work scenarios, this old evaluation logic is obviously no longer sufficient.

Just imagine a very ordinary task in a factory: "Walk to the shelf to pick up an item, then deliver it to the designated location."

It sounds easy, but if executed by a robot, this simple task can be deconstructed into several challenges — the robot needs to judge where the item is and how to grasp it; it needs to respond to unexpected situations in real time during the handling process, and avoid people passing by suddenly; when the staff temporarily changes the task, it also needs to understand the new requirements.

In other words, a small task involves operation capability, interaction capability, and motion capability, none of which is dispensable.

A number of core achievements launched by Agibot this time, such as GO-3 Preview, WITA-OMNI, and AGILE, are developed based on the basic pain points of real implementation.

The core significance of GO-3 Preview is to enable robots to better understand the actions they are about to perform.

In recent years, VLA has become an important technical direction for embodied intelligence, allowing robots to understand the environment and instructions and generate actions just like large models understand text.

However, traditional VLA has a bottleneck: even if the robot knows what to do next, it may not be clear about the consequences of this action. For example, when it wants to pick up the cup on the table, it may knock down the objects next to it.

The core breakthrough of GO-3 Preview is the integrated decision-making mechanism of "predict first, then act". That is to say, after receiving the task, the robot will first integrate the images from multiple cameras and instructions to figure out "what to do and what the environment is like"; then deduce several possible situations internally; finally select the most reliable solution to act. It is equivalent to thinking twice before acting, instead of thinking while doing.

In addition to continuously exploring the embodied intelligence brain model, Agibot has also targeted an indispensable important direction that has been collectively ignored by giant foundation model companies this time — the full-modal end-to-end interaction large model. "Interaction is exactly the necessary and first entry point for robots to generate value, and all scenarios are inseparable from interaction," said Xiong Yan, Partner, Co-President, President of the Central R&D Department, and President of the Interaction Business System of Agibot.

In the past, the interaction mode between humans and robots was basically that you had to call its name to "wake it up", then ask and answer, and wait for the response. The WITA-OMNI large model completely changes the underlying logic of human-robot interaction.

At the launch site, Xiong Yan had an interesting interaction with the robot Yuanzheng A3 equipped with this model — no wake-up word is required. In the scene with complex noise, the robot can still accurately judge whether you are talking to it, and even if you quickly interrupt and switch questions, the interaction will not be affected.

According to on-site tests, the WITA-OMNI model has a fastest response speed of only 400 milliseconds, supports more than 20 kinds of emotional expressions, and can understand 73 languages. With the support of the WITA-OMNI model, robots have finally changed from passive machines that only listen to instructions to chatty partners.

It is worth noting that both of these two models have delivered excellent results recently.

According to the introduction, GO-3 Preview has topped three industry authoritative evaluation lists: RoboDojo, RoboTwin, and RoboColiseum, and will be fully open-sourced by the end of 2026; WITA-OMNI has won the first place in four lists: Daily-Omni full-modal understanding, TimeLens-Bench video temporal positioning, EmbSpatialBench embodied spatial reasoning, and MMAU complex audio understanding.

With the brain that "thinks before acting" and "is good at chatting", robots also need a body that can keep up.

The AGILE released by Agibot this time is a brand new general motion intelligence algorithm framework. It can be understood this way: robots can, like humans, see changes with their eyes, and their bodies can immediately make appropriate responses in one go.

From GO-3 Preview to WITA-OMNI and then to AGILE, Agibot is simultaneously advancing the three lines of operation intelligence, interaction intelligence, and motion intelligence — enabling robots to not only know what to do, but also do it steadily, and cooperate with humans naturally.

However, having all three capabilities of thinking, moving, and chatting does not mean that the robot can operate smoothly. They must be connected in real time on the same time axis and the same state space. Otherwise, the following situation may occur: the brain issues an instruction, but the cerebellum cannot receive it; the cerebellum receives the instruction, but the interaction drops offline.

Collaboration is exactly the next threshold for the implementation of embodied intelligence beyond the "three intelligences".

The Next Threshold for Robot Implementation Lies Beyond Models

Collaboration cannot just be a slogan. It is necessary to truly start from the bottom layer and design a special "adhesive" for robots to organize all parts organically.

At the Agibot Innovation Day, another core innovative achievement of Agibot is the third-generation hardware architecture AHA3. It is not patching the old solution, but redesigning a unified control platform from the bottom that can carry the three intelligences of motion, interaction and operation at the same time, so that the "three intelligences" are truly connected at the physical level.

This is also a design that hits the pain points of the industry. In the past few years, the biggest problem in hardware manufacturing in the robotics industry was fragmentation — models and systems operated independently, and were barely connected by cables and protocols. This also led to the fact that in the past, the robot body was filled with controllers, which were large, heavy, and had poor heat dissipation. According to Agibot's disclosure at the launch event, the direct benefits brought by this system are: the volume of the control system is reduced by half, and the response speed is nearly doubled.

However, only redesigning the hardware architecture, which is the skeleton, is far from enough. A better "connective tissue" is also needed.

For a long time, robots are still in the state of manual assembly. The root cause is that there are a large number of different sensors, controllers and actuators on robots. These hardware come from different manufacturers and adopt different interfaces and communication methods. For developers, this is undoubtedly a disaster — if a large amount of underlying code needs to be rewritten every time a new device is connected, the development and upgrade of products will be very complicated.

Therefore, the role of Agibot's Lingqu OS 1.0 is to connect all three layers of hardware, communication and intelligence, so that robots have a unified operating system for the first time. It is worth noting that this is also the industry's first native operating system for embodied intelligence.

For engineers, the most tangible change is that Lingqu OS 1.0 directly makes all hardware access as simple as plugging in a U disk — by redefining the hardware access standards, more than 100 types of peripherals in 15 categories, including cameras, sensors, and robotic arms, can be used as soon as they are plugged in, without the trouble of processing drivers.

In addition to the significance of interconnection, Lingqu OS 1.0 is also extremely fast. When the robot sends 1000 motion instructions per second, Lingqu OS 1.0 can keep the error of each interval less than 4 microseconds. For example, in the time it takes for a human to blink, the robot has sent more than 300 instructions, each of which arrives on time with millisecond-level accuracy.

However, Agibot does not plan to enjoy the convenience of this infrastructure exclusively.

At the Agibot Innovation Day, Agibot announced that it will build an open source community for humanoid robots in China together with the OpenAtom Open Source Foundation, promote technology transformation and industry standard formulation in core directions such as ontology hardware, data evaluation, operating systems, and large and small brains, and donate the Lingqu OS project to the foundation. The two sides will organize joint development, testing and verification, and embodied intelligence talent training to build a globally open and prosperous technology ecosystem.

At the same time, Lingqu OS 1.0 is planned to be fully open-sourced by the end of 2026. At that time, about 200 million lines of code and more than 5,000 ecological software packages that have been verified in mass production will be opened to promote robot capabilities to become a reusable industrial foundation.

For developers, Lingqu OS 1.0 is equivalent to packaging a large number of intelligent capabilities into "skill packs". Developers do not need to build everything from scratch, and can directly realize out-of-the-box use of the three major directions of motion intelligence, interaction intelligence and operation intelligence.

"You can never imagine what surprises our partners will bring us, and that's the charm of the ecosystem. So we must build a good foundation and leave more opportunities to our partners," said Wang Chuang, Partner, Senior Vice President, and President of the General Business Department of Agibot.

Looking back at the core content released on Agibot Innovation Day, Agibot has basically realized self-development and interconnection of the three layers of model, hardware and system. This technical depth and coverage breadth are actually rare in the embodied intelligence industry.

The ultimate examination room for this complete system may not be in the laboratory, but in every scenario of real implementation.

General Robots Start to Work, and a New Ruler Is Needed

Over the past decades, industrial robots have a mature and simple method to calculate whether the investment is worthwhile. Just calculate how many parts this machine can produce per hour, multiply by the unit price of the parts, subtract the costs of electricity and maintenance, and you can quickly get the payback period.

However, when general embodied intelligence robots are implemented, the industry urgently needs a new ruler to measure value — because the jobs of embodied intelligence robots are not fixed. They may be screwing on the assembly line today, and be responsible for logistics assembly tomorrow.

Therefore, after solving the basic technical layout, Agibot also tries to explore a unified "measurement standard" for the new quality