BrainCo, a company specializing in the development of brain-computer interfaces (BCI), has demonstrated a new platform that enables robot control via neural signals.
This technology was presented at the World Artificial Intelligence Conference (WAIC) in 2026, held in Shanghai, China. During the demonstration, one participant was able to control a robotic arm solely by thought.
The user wore a lightweight electroencephalography (EEG) headset connected to the company's BCI system. When the user mentally imagined picking up a cap, the robotic arm recognized this intention and automatically performed the action. According to BrainCo, the entire process, from reading brain signals to the robot executing the movement, takes less than 200 milliseconds.
The technology functions in three stages. First, the EEG headset records electrical signals emitted from the user's brain. Next, artificial intelligence (AI) algorithms analyze this information to determine the intended movement or control. Finally, this intention is converted into commands sent to the robot to perform the task.
Nick He, partner and Senior Vice President of BrainCo, noted that the system is the result of ten years of research in BCI. He stated in The Robot Report: 'A decade of BCI research has given us the ability to decode what a person intends to do and translate it into machine action.'
He also added that the combination of various technologies will trigger a new stage of human-machine interaction. 'By integrating brain-computer interfaces, AI, and embedded AI, we believe this will define the next chapter of human-machine collaboration.'
During the presentation in Shanghai, the robotic arm also performed more complex tasks requiring high precision, such as holding a glass and picking up an apple.
According to the company, the platform is designed to be compatible with various commercially available robots, including humanoid robots, robotic arms, and legged robots. The goal is for manufacturers and research centers to implement this technology in their projects without dependence on proprietary hardware.
BrainCo, based in Somerville, Massachusetts, describes the platform architecture as 'neuro-embodied AI.' In this model, the BCI determines the operator's intention, and the AI layer interprets this intention and breaks it down into executable steps. The robot systems themselves are then responsible for the physical execution of the task.
In addition to the neural signal control platform, BrainCo introduced its new AI-embodied data collection solution at WAIC. This initiative aims to solve one of the main problems in modern robotics—the lack of real, high-quality data for training AI systems capable of performing complex and delicate tasks.
BrainCo explains that training a robot to perform actions such as folding clothes, assembling components, or manipulating fragile objects requires huge volumes of data gathered in real-world conditions, which remains a barrier to the development of this field.
To address this, the company developed its own platform consisting of a mobile data collection system equipped with two robotic arms and a high-precision glove. This complex records information from three sources: actions performed by the robot; demonstrations performed by human operators; and virtual simulations. Furthermore, the system records the EEG signals of the human operator. Thus, the platform captures not only the human hand movements but also the brain commands that generated those movements.
According to BrainCo, this approach combines the quality of data obtained from real robots with the scalability of human demonstrations, allowing for the continuous generation of large volumes of data based on real-world tasks.
Founded in 2015, BrainCo already sells three products in the fields of robotics and intelligent prosthetics. These include the Revo 3 Dexterous Hand, a robotic end-effector with 21 degrees of freedom. The company also offers the Intelligent Bionic Hand, a five-fingered prosthetic hand, and the Intelligent Bionic Leg, a smart knee joint for lower limb prosthetics.
Chinese companies are implementing humanoids in real-world scenarios to collect data and develop machines capable of performing human tasks, marking an ambitious strategy by the country.
According to Bloomberg, current investment is concentrated on embodied intelligence, a technology that combines robotics and artificial intelligence to create equipment better suited to the physical environment. Examples include a humanoid robotic arm organizing snack packages on a shelf near Beijing, or workers recording simple actions, such as folding sheets or removing pillows from sofas, whose images will be used to refine the robots' AI systems.
This evolution signals a shift in focus for Chinese industry. After gaining attention with the martial arts movements of the Unitree Robotics G1 model, manufacturers have started prioritizing the machines' learning capacity. Giants like Alibaba and Xiaomi, along with sector startups, are creating embodied intelligence models that allow robots to acquire knowledge through physical experiences.
The Chinese approach differs from the American one, where companies use purchased data, simulations, and low-cost labor in countries like India and Vietnam. Chinese manufacturers, on the other hand, seek to place their own robots in authentic situations to generate self-learning.
The presence of industrial robotics in China is already notable. Data cited by Bloomberg indicates that the country installed about 300,000 robots in 2024, compared to approximately 38,000 in the United States. Due to population aging, Beijing sees humanoids as a way to mitigate the impacts of a potential labor shortage. Barclays analysts project that such machines could cover up to 60% of a future personnel deficit.
Several initiatives are underway: the Ministry of Industry and Information Technology of China plans to introduce 10,000 humanoid robots into factories by the end of the year; investors have already allocated at least 100 billion yuan (equivalent to about 82 billion Brazilian reais) to the sector in 2025, surpassing the amount invested in the previous five years; furthermore, data collection centers are being established to train machines in domestic environments, stores, and assembly lines. Chinese companies intend to use robots in real contexts to accumulate millions of hours of learning experience.
Developing more sophisticated robots demands a vast amount of data. Unlike language models, these machines need to learn complex motor skills, such as handling delicate items or preventing cups from falling. Jacqueline Du, a Goldman Sachs analyst in Hong Kong, reported that leading companies have already achieved about 500,000 hours of collected data.
China has inaugurated 64 data collection centers and has 20 others under construction, replicating scenarios such as supermarkets, offices, factories, and residences. Gan Ruyi, head of algorithms at X Square Robot, emphasized that this is an area where the US does not have an advantage, highlighting that the Chinese capacity to organize workers and deploy machines on a large scale may be a distinctive factor.
In the United States, companies like Tesla, Figure AI, Apptronik, and Agility Robotics are also making progress with tests. Figure AI demonstrated the separation of nearly 60,000 packages in 50 hours of live transmission, achieving a speed close to that of a human worker. However, Chinese companies criticized this test for considering it overly controlled. Ai Wen, project director at Agibot, argued that the Figure demonstration still takes place in a laboratory environment, while Chinese robots are trained directly on production lines. Bloomberg concludes that the future of humanoids will depend on the ability to convert everyday experiences into learning for progressively more autonomous machines.
