Optimizing Robot Programs with Deep Learning

While industrial robots allow to commission, program, deploy, monitor and adapt flexible production cells much more quickly and economically than traditional automation, those savings are largely due to reduced requirements of custom hardware and high potential for standardization. Their inherent flexibility means that parts of the costs are now shifted to the programming and deployment stages of the robot cell lifecycle – where adaptive force control or vision-based robot skills are now realized in software. The ArtiMinds RPS allows robot programmers to quickly program robots to solve complex tasks by combining pre-parameterized robot skills, with dedicated skills for force- or vision-controlled sensor-adaptive tasks.

To further increase the cost-effectiveness of skill-based robot programming, we developed and patented a data-driven solution to automatically self-optimize robot skill parameters using machine learning. Shadow Program Inversion (SPI) was presented at the 2021 IEEE International Conference on Robotics and Automation (ICRA).

Click here (PDF) to download the white paper, and here for the conference paper and additional material.