ABB Robotics has announced a strategic investment in California-based LandingAI, aiming to accelerate the integration of vision-based artificial intelligence into its robotic systems. The partnership will see LandingAI’s visual AI platform, including LandingLens™, embedded within ABB’s existing robotics software, enabling faster deployment and broader application of robotic vision technologies across multiple sectors.
According to ABB, the collaboration is intended to reduce the time required to train and deploy vision AI systems by up to 80 percent. These systems allow robots to detect objects, patterns, or defects without the need for advanced programming or AI expertise. ABB stated that this capability will enable end users and system integrators to retrain AI models for new tasks independently, supporting greater adaptability in environments such as logistics, healthcare, and food and beverage operations.
The investment was made through ABB Robotics Ventures, the company’s strategic venture capital arm. Financial details of the transaction were not disclosed.
ABB described the partnership as part of its broader strategy toward Autonomous Versatile Robotics (AVR™), a concept focused on increasing the autonomy and flexibility of robotic systems. The company is already piloting LandingAI’s technology for applications such as item-picking, sorting, depalletizing, and quality inspection.
LandingAI, founded by AI researcher Andrew Ng, provides tools designed to make computer vision applications more accessible to non-experts. Its platform includes no-code tools and pre-trained models intended to speed up development and deployment.
ABB also highlighted that it remains the only robotics company to offer a fully integrated AI training tool within its software environment. The tool will be available alongside RobotStudio®, ABB’s simulation and programming software that includes digital twin functionality.
The collaboration reflects growing demand for AI-enhanced robotics in industries where operational speed, flexibility, and reduced reliance on specialist skills are critical. ABB said this demand is driving the shift toward more autonomous systems capable of adapting to a wider range of tasks outside traditional manufacturing.
