For example, Siemens’ SIMATIC Robot Pick AI expands on this vision of adaptability, transforming standard industrial robots—once limited to rigid, repetitive tasks—into complex machines. Trained on synthetic data—virtual simulations of shapes, materials, and environments—the AI prepares robots to handle unpredictable tasks, like picking unknown items from chaotic bins, with over 98% accuracy. When mistakes happen, the system learns, improving through real-world feedback. Crucially, this isn’t just a one-robot fix. Software updates scale across entire fleets, upgrading robots to work more flexibly and meet the rising demand for adaptive production.
Another example is the robotics firm ANYbotics, which generates 3D models of industrial environments that function as digital twins of real environments. Operational data, such as temperature, pressure, and flow rates, are integrated to create virtual replicas of physical facilities where robots can train. An energy plant, for example, can use its site plans to generate simulations of inspection tasks it needs robots to perform in its facilities. This speeds the robots’ training and deployment, allowing them to perform successfully with minimal on-site setup.
Simulation also allows for the near-costless multiplication of robots for training. “In simulation, we can create thousands of virtual robots to practice tasks and optimize their behavior. This allows us to accelerate training time and share knowledge between robots,” says Péter Fankhauser, CEO and co-founder of ANYbotics.
Because robots need to understand their environment regardless of orientation or lighting, ANYbotics and partner Digica created a method of generating thousands of synthetic images for robot training. By removing the painstaking work of collecting huge numbers of real images from the shop floor, the time needed to teach robots what they need to know is drastically reduced.
Similarly, Siemens leverages synthetic data to generate simulated environments to train and validate AI models digitally before deployment into physical products. “By using synthetic data, we create variations in object orientation, lighting, and other factors to ensure the AI adapts well across different conditions,” says Vincenzo De Paola, project lead at Siemens. “We simulate everything from how the pieces are oriented to lighting conditions and shadows. This allows the model to train under diverse scenarios, improving its ability to adapt and respond accurately in the real world.”
Digital twins and synthetic data have proven powerful antidotes to data scarcity and costly robot training. Robots that train in artificial environments can be prepared quickly and inexpensively for wide varieties of visual possibilities and scenarios they may encounter in the real world. “We validate our models in this simulated environment before deploying them physically,” says De Paola. “This approach allows us to identify any potential issues early and refine the model with minimal cost and time.”
This technology’s impact can extend beyond initial robot training. If the robot’s real-world performance data is used to update its digital twin and analyze potential optimizations, it can create a dynamic cycle of improvement to systematically enhance the robot’s learning, capabilities, and performance over time.
The well-educated robot at work
With AI and simulation powering a new era in robot training, organizations will reap the benefits. Digital twins allow companies to deploy advanced robotics with dramatically reduced setup times, and the enhanced adaptability of AI-powered vision systems makes it easier for companies to alter product lines in response to changing market demands.