Stem cell therapy is a promising approach to treat heart insufficiency and benefits from automated myocardial injection which requires highly precise motion of a robotic manipulator that is equipped with a syringe. This work investigates whether sufficiently precise motion can be achieved by combining a SCARA robot and learning control methods. For this purpose, the method Autonomous Iterative Motion Learning (AI-MOLE) is extended to be applicable to multi-input/multi-output systems. The proposed learning method solves reference tracking tasks in systems with unknown, nonlinear, multi-input/multi-output dynamics by iteratively updating an input trajectory in a plug-and-play fashion and without requiring manual parameter tuning. The proposed learning method is validated in a preliminary simulation study of a simplified SCARA robot that has to perform three desired motions. The results demonstrate that the proposed learning method achieves highly precise reference tracking without requiring any a priori model information or manual parameter tuning in as little as 15 trials per motion. The results further indicate that the combination of a SCARA robot and learning method achieves sufficiently precise motion to potentially enable automatic myocardial injection if similar results can be obtained in a real-world setting.

Traditional robotic systems require specific training data for each task, environment, and robot form. While recent advancements in machine learning have enabled models to generalize across new tasks and environments, the challenge of adapting these models to entirely new settings remains largely unexplored. This study addresses this by investigating the generalization capabilities of the RT-1-X robotic foundation model to a type of robot unseen during its training: a SCARA robot from UMI-RTX. Initial experiments reveal that RT-1-X does not generalize zero-shot to the unseen type of robot. However, fine-tuning of the RT-1-X model by demonstration allows the robot to learn a pickup task which was part of the foundation model (but learned for another type of robot). When the robot is presented with an object that is included in the foundation model but not in the fine-tuning dataset, it demonstrates that only the skill, but not the object-specific knowledge, has been transferred.

Which robot applications are best suited for the electronics industry? Robots are becoming extremely popular among electronic companies. Recent estimates indicate that robot growth is even higher in the electronics industry than in the automotive industry, which has traditionally been the leader for robot use. If you are looking to add robotic automation to your process, it can be hard to know which tasks are the best candidates. Classic application in electronics manufacturing, robots are perfect for circuit board assembly.

Machine automation could be compared to the human body. Our eyes are the sensors that monitor operations. Our hands are the actuation to manoeuvre things around us. Finally, our brains are the process control, providing intelligence and managing processes. Traditionally, machines in industrial environments could only provide actuation, but not anymore.

Once found mostly in manufacturing environments with extremely high volumes, robots are now being used in smaller organizations, and in a wider variety of applications. The cost of implementing robotic systems has fallen significantly, plus it is now easier to apply robotics in more ways. The reason is simple: over the last few years, controls have become more user-friendly, requiring fewer programming resources. System design has become easier with the availability of online tools to help end-users and OEMs build systems directly. Servicing has also become easier and faster thanks to standardized components and powerful diagnostics.