Metzingen-based NEURA Robotics today introduced Neuron AI, its multimodal foundation model for Physical AI – combining 3D vision, touch, audio, robot state and language with memory – enabling their robots to act on information beyond what the camera sees and even retain context when it disappears from view.
This comes off the back of their previously announced a Series C of up to €1.2 billion in June 2026, following its €120 million Series B in 2025, to support the development and deployment of cognitive robotics, shared intelligence infrastructure and Physical AI systems such as Neuron AI.
“An intelligent robot needs a brain that brings its senses together and remembers what happened,” says David Reger, Founder and CEO of NEURA Robotics. “With Neuron AI, we are building the brain of the physical world: Physical AI that turns human expertise and physical experience into capabilities robots can learn and apply. Developed in Germany, built for the world.”
This AI launch follows a series of European investments in Physical AI and industrial robotics during 2026.
In Germany, Stuttgart-based Sereact secured €93 million to expand its AI-powered robotics platform, while Munich-based RobCo raised €89.2 million for industrial automation.
Elsewhere, London-based Humanoid raised €133 million, Barcelona-based THEKER secured €73 million, and Norway- and US-based Trener Robotics announced €26 million, bringing the total relevant funding in Europe across 2026 to approximately €414.2 million (not including Neura’s funding).
Neuron AI was developed and trained in-house in Metzingen, Germany. Founded in 2019, NEURA brings together the robots, hands, sensing technology, data infrastructure and AI development needed to connect model training with physical performance.
Germany’s industrial base provides an important setting for this work: complex manufacturing processes, specialised expertise and demanding operating environments. Data partnerships with industrial companies including Schaeffler and Bosch complement training in the NEURA Gyms.
The NEURA Gyms connect data collection, skill training and benchmarking before deployment. Training data and skills developed in the Gyms inform subsequent model releases.
For NEURA, this proximity connects the development of Physical AI with the knowledge and practical requirements of the industries it is intended to serve.
Rather than treating each sensor as a separate input, Neuron AI encodes 3D vision, tactile and force data, audio, robot state and language, aligns them in one shared space over time, and turns them into a single stream of action.
The company explains that multisensory perception matters most for tasks that cameras cannot solve on their own. When a robot opens a lock, only sound and touch confirm that it is really open. With touch, a robot can grasp objects it cannot see. The additional senses also improve tasks that vision can already handle: in welding, for example, the quality of a seam can be judged from the sound of the process.
In addition, Neuron AI has a memory function. To act in the physical world, a robot needs a sense of cause and effect and object permanence, the ability to remember objects when they are out of sight, which humans acquire in their first two years of life.
Unlike purely digital AI, a robot acts in a continuous physical world and must keep track of objects and the consequences of its own actions over time, often without seeing them. NEURA adapted the model’s architecture specifically to introduce memory and trains it on dedicated datasets that make use of it.
Neuron AI is trained on three complementary sources of physical experience:
- Real-world robot data: Robots across different embodiments perform tasks in NEURA Gyms, NEURA’s training infrastructure for cognitive robots and Physical AI and real-world environments, generating synchronised vision, touch, force, audio and movement data.
- Simulation-amplified data: NEURA’s Data Amplifier and simulation stack expand training data through simulated variations of tasks and environments.
- Human demonstration data: People wearing sensor-equipped data suits capture human movements and task demonstrations, bringing practical expertise into the training pipeline.
Together, these pillars combine real physical interactions, simulated variation and human know-how to train a shared foundation across five NEURA robot embodiments, with different grippers and hands.
NEURA will demonstrate its humanoid robot 4NE1 (“For Anyone”) at NVIDIA GTC Berlin 2026 performing an eleven-minute laundry-handling sequence with the current version of Neuron AI.
The sequence includes sorting laundry, loading a washing machine, closing and reopening it, unloading it and transferring the laundry to a dryer, without human intervention.
