Sereact raises $110 million to bring embodied AI into real-world scenarios

At present, the embodied intelligence industry is already booming, but the products of many companies still remain at the stages of demonstration videos, laboratory environments, and POC.

Take Unitree as an example. Its humanoid robots are still mainly targeted at the scientific research and education market. According to the prospectus, from January to September 2025, the revenue from scientific research and education of Unitree's humanoid robots accounted for 73.60%, and the revenue from industry applications accounted for 9.01%. The entry into production scenarios is still in its early stage.

A German embodied intelligence company chose a path for the accelerated implementation of embodied intelligence from the very beginning. Since its establishment, its products have been operating in real production environments. Currently, it has top - tier enterprise customers such as Austrian Post, BMW, Daimler Truck, and Mercedes - Benz.

This company is Sereact. It recently received $110 million in Series B financing, led by Headline, with participation from Bullhound Capital, Felix Capital, and Daphni. Old shareholders Air Street Capital (the lead investor in the seed round), Creandum (the lead investor in Series A), and Point Nine continued to follow - on invest.

In its earlier investment rounds, there were also angel investors with backgrounds in robotics and industrial automation, such as Oliver Cameron, the former vice - president of product at Cruise and co - founder of Voyage, and Mostafa Elsayed, the CEO and co - founder of Automata.

An interesting statistic is that Sereact has deployed over 200 sets of Cortex systems, completing over 1 billion pick - and - place operations in real production environments. On average, only one remote human intervention is required for approximately every 53,000 pick - and - place operations.

What makes Sereact worthy of attention is that it has placed robots in the real world from the start, allowing them to perform specific tasks in warehouses and production lines, generate specific value, and accumulate real - world data.

An Embodied Intelligence System that Learns in the Real World

Sereact was founded in 2021. Its two co - founders, Ralf Gulde and Marc Tuscher, both graduated from the University of Stuttgart and previously conducted cross - research on robotics and artificial intelligence at the Institute for Machine Tools and Manufacturing Unit Control Engineering (ISW) of the university.

Ralf Gulde (CEO) focuses on research areas including reinforcement learning, robot manipulation, and the deployment of real - world robot systems; Marc Tuscher (CTO) specializes in deep learning, computer vision, and robot software systems.

The two conducted joint research on the combination of AI and robotics at the University of Stuttgart and finally commercialized this research path in 2021 by founding Sereact.

Under their leadership, Sereact didn't start by developing general - purpose robots for open home environments. Instead, it chose specific, less "glamorous" but highly practical and valuable aspects in warehousing and manufacturing, such as picking, packing, returns processing, sorting, and inventory verification.

The warehousing environment is sufficiently enclosed, tasks can be clearly defined, KPIs can be quantified, and customers are willing to pay for efficiency. At the same time, real - world warehouses present numerous complex situations. These factors make warehousing not only a commercial scenario but also a high - density data field for training robots to understand the physical world.

One of Sereact's most representative early products is PickGPT. It is a robot Transformer model aiming to enable robots to pick and manipulate objects in real - world physical scenarios through natural language and visual understanding.

PickGPT addresses the first hurdle for robots to enter the field: interaction and deployment. Front - line employees can assign tasks to robots via voice or the interface. When facing new objects and tasks, robots don't need to be retrained from scratch.

After PickGPT, Sereact advanced its technical roadmap to Cortex.

Cortex is a vision - language - action model, namely VLA. It aims to solve more than just enabling robots to "understand language." It integrates vision, language, and action into a single system, allowing robots to perform tasks in real - world physical environments.

Sereact's judgment on Cortex is based on real - world warehousing operations: fulfillment centers have messy bins, long - tail SKUs, reflective packaging, flexible objects, occlusions, and abnormal placements. To generalize in such an environment, the model must be trained on real - world operational data and combine visual perception, task understanding, and motion control.

Therefore, Cortex adopts a hierarchical design: first understand the task and visual scene, generate a 3D plan, and then adapt it to the control space of specific robots through the motion control module.

In 2026, Sereact successively updated Cortex 1.5, Cortex 1.6, and Cortex 2.0 within a few months.

Put simply, Cortex 1.5 addresses failure recovery through policy patching; Cortex 1.6 makes the daily operation process learnable through process rewards; Cortex 2.0 enables robots to predict the future before taking action through a world model.

Cortex 1.5 solves the distribution shift problem of the basic Cortex VLA in real - world deployment: although regular pick - and - place operations can be completed, robots lack stable failure - recovery capabilities when encountering edge cases such as rare postures, process stalls, or object slippage.

It introduces "interactive reinforcement learning policy patching": when a robot fails at a certain node, a human operator provides a short error - correction demonstration via remote control. The system then absorbs this demonstration as a local policy update instead of retraining the entire model. The updated policy can also be synchronized to the robot cluster, allowing single - point experience to be reused across multiple deployment nodes.

Cortex 1.6 further reduces the reliance on human intervention by introducing PRO, the process reward operator.

In the past, many robot learning methods only considered the final result: success or failure. However, in real - world operations, a large amount of valuable information is hidden in the process, such as sliding, lag, collision risk, abnormal force feedback, or partially correct actions completed before failure. PRO extracts continuous learning signals from real - world operations, enabling the daily operation process itself to be used for model optimization.

Sereact claims that in tasks such as pick - and - place, shoe - box opening, and returns processing, the success rate of Cortex 1.6 is approximately 98%.

Image source: Sereact

The latest Cortex 2.0 upgrades robots from reactive systems to proactive systems: from "look then pick" to "predict then act."

It combines the VLA model with a world model, enabling robots to generate multiple candidate trajectories based on the current state, predict the results, score them according to stability, risk, and efficiency, and only execute the optimal plan.

Put more simply, past robots were more like trial - and - error learners: they would retry if a pick failed, and might repeatedly fail if the strategy was wrong.

Cortex 2.0 allows robots to make judgments before taking action: whether an object will slip when picked, whether it will hit the box wall when placed, and whether it will get stuck a few steps later. This is especially important for long - term tasks such as packing, kit assembly, returns shelving, precise placement, and assembly, as small errors can be magnified in subsequent steps, leading to collisions, jams, or human intervention.

Returns processing is a typical example. It is far more complex than pick - and - place: robots need to successively complete tasks such as scanning barcodes, unpacking, unfolding products, checking product conditions, repackaging, and deciding whether products should be restocked, refurbished, or discarded. Each step depends on the information from the previous step, and the state of products is highly uncertain.

The value of Cortex 2.0 lies in enabling robots to predict results before execution, adjust the product posture actively with the help of the Lens vision system to obtain a better inspection perspective, and then make decisions on product condition assessment and product flow. It upgrades robots from "being able to pick an object" to "being able to understand and manage a process."

Image source: Sereact

What supports this evolution is Sereact's real - world learning closed - loop. Successful pick - and - place operations, failed attempts, and error - correction actions of robots at customer sites are all recorded together with visual observations, robot states, gripper force feedback, and final results for model updates. After new policies pass automated regression tests, they are pushed to the entire robot cluster.

The resulting data flywheel is as follows: the more deployments there are, the more real - world interactions occur; the more data there is, the more stable the model becomes; the more stable the model is, the more customer sites it can enter.

This is also the most crucial aspect of Sereact's approach. Simulations and laboratory demonstrations can verify the direction, but it is difficult to cover all the weights, frictions, reflections, occlusions, packaging deformations, and abnormal events in real - world warehouses. Truly reliable robot AI still needs to learn from real - world failures at production sites.

Embodied Intelligence Needs to Find High - Value Implementation Scenarios

Embodied intelligence has attracted a huge amount of capital. According to the statistics of QbitAI Think Tank, the total financing amount in the embodied intelligence track in the Chinese market in 2025 was approximately 55.4 billion yuan. Xiniu Data shows that the financing scale of the embodied intelligence track in Q1 2026 reached 24.373 billion yuan. The capabilities of hardware and software are also continuously improving. However, the real threshold that the industry needs to cross is to enter production - oriented commercial scenarios and form a sustainable value closed - loop.

Embodied intelligence can only have a foundation for continuous development of the entire ecosystem when it generates value in specific scenarios. Just as large - language models have proven their value in scenarios such as coding and vertical agents, the popularity of Claude Code, "Lobster," and Codex this year, as well as the recent $950 million financing of Sierra, all indicate that scenarios with high frequency, verifiable results, and customers willing to pay are more likely to achieve commercial validation first.

Therefore, embodied intelligence may not need to pursue open, complex, and human - like scenarios such as home environments from the start. Home tasks are vague, safety requirements are high, user tolerance for errors is low, and the business model is not clear. In contrast, closed scenarios for production and fulfillment have clearer task boundaries and the value is easier to calculate.

This is similar to autonomous driving: fully autonomous driving on open roads is still challenging, but ports, mines, and closed industrial parks have generated value earlier. Embodied intelligence may follow a similar path, first completing some tasks in high - frequency, controllable, and measurable scenarios to form deployments, data, and revenue.

Entrepreneurship in the field of embodied intelligence in China is already quite active. The key in the next stage lies in large - scale verification in offline scenarios. China has a huge manufacturing system, logistics network, and industrial supply chain. These scenarios are complex enough and the commercial value is clear enough, making it easier to form a continuous closed - loop.

Whoever can stably deploy robots into these scenarios first may be the first to build the data flywheel of embodied intelligence.

This article is from the WeChat official account "Alpha Startups" (ID: alphastartups). The author is someone who discovers extraordinary entrepreneurs. It is published by 36Kr with authorization.