RoboDK in Physical AI Research
Researchers use RoboDK to enable a variety of AI and machine learning (ML) applications. Recent examples include an AI/ML-enabled gluing application that uses RoboDK for motion planning, a vision-based safety system for human-robot collaboration that uses RoboDK to monitor robot motion, and a robotic kitting system for battery cells that uses AI vision and RoboDK's digital twin features.
Other research includes a policy-aware LLM framework tested through multi-robot mission simulations in RoboDK, and research that used a BiLSTM neural network to generate smoother, more accurate UR3 robot trajectories from image-based contours, with RoboDK used for simulation and robot programming.
AI/ML-Enabled Gluing
In 2026, researchers unveiled an AI/ML-enabled gluing system in which YOLOv8 and RealSense vision are used to identify a component's position and orientation, while RoboDK dynamically transforms reference frames and aligns curves to the actual part position and rotation.
View the research.
Self-Learning Robotic Welding
Also in 2026, a team of researchers used RoboDK's digital twin functionality as part of a self-learning robotic welding system. A genetic algorithm optimized the robot's operating position, reducing simulated energy consumption by 36%.
View the research.
