ABB Robotics Adds Vision-Based Physical AI to Fashion Inductor Logistics Module
- Published
ABB Robotics has launched its first end-to-end, vision-based industrial-grade physical AI solution by integrating AI Robot Trainer into its Fashion Inductor module. The system is designed to help system integrators deploy, scale and continuously optimize AI-powered fashion logistics applications with less effort and risk. Fashion Inductor handles sorting in variable apparel logistics environments, with throughput of up to 1,300 items per hour per cell and picking accuracy targets above 99.5 percent.
AI Robot Trainer adds pre-trained, fashion-specific vision models, application-specific tools and rapid retraining capabilities. ABB Robotics says these features can reduce engineering and commissioning effort by up to 75 percent and allow applications to be adapted without specialist AI expertise. The upgraded module also includes a digital twin that connects the Application Controller Platform, Automatic Path Planning, AI Robot Trainer and RobotStudio. It supports commissioning and flexible setup, including integration of third-party components such as grippers. A closed-loop workflow monitors performance, identifies anomalies, generates training data and deploys model improvements to the physical robot.
Fashion Inductor is the first step in ABB Robotics’ broader rollout of vision-based physical AI as part of its development of more autonomous and versatile robotics. The system lifecycle uses RobotStudio for virtual design, simulation and validation, while the OmniCore robot controller serves as the production execution layer. ABB Robotics did not disclose a further rollout timeline.
Original report
ABB Robotics brings industrial-grade physical AI to fashion logistics
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