The active support of Unity’s ecosystem would ensure that any potential issue would be addressed properly.Īt the end of our analysis, Unity emerged as our best option for its versatility and evolvability. In addition, Unity offers regular updates as well as contributions from collaborators to tailor the engine to more advanced applications such as our own. To save development time, we could directly rely on its existing file importer, rendering system, physics engine, script lifecycle, scheduler, and deployment options. Notably, Unity allowed us to focus on creating only the features needed for training AIs on robots, while leaving the rest to the engine. Robot manipulation would still be possible at a higher level of control, which matched our strategy of developing hardware agnostic solutions. Here, we discovered that game engines responded well to this variety of demands, as they offer simple answers to these other constraints. In the context of AI, although we indeed require accurate physics and perfect control over the robot’s behaviors, we also need to import a wide range of objects of different shapes, with realistic textures and visuals, such as lights, shadows, camera effects, and so on. We make use of Unity’s High Definition Render Pipeline (HDRP) and the Shader Graph workflow to create variations in sky conditions, lights, backgrounds, and object textures. Meteorological Domain Randomization (MDR) for robotic applications. Given this dichotomy, how might we introduce the latest AI solutions into such a precise, constrained environment? And how might we experiment with, validate and deploy AI solutions in a way that doesn't introduce risk, cost, downtime, or some combination of all three? These are the questions we were asking ourselves when tasked with training and deploying AI on our clients' factory floors. In the manufacturing environment, a remaining 1% error rate is an unacceptably high failure, defect, or safety risk that can have severe real-world consequences. In a lab, reaching 99% accuracy is a laudable achievement. The two industries couldn't be further apart in their approaches. In stark contrast to manufacturing, AI rarely takes the time to validate itself under real-world conditions. However, most of this research, only exists in the lab, built upon carefully curated data that bears little resemblance to the noisy, unstructured, unlabeled, or as is often the case, the complete absence of data that exists in the real world.
Every other day brings new research on the latest methods, expanded possibilities, and new frontiers. This results in zero room for experimentation, disruption, risk, or unproven methods.ĪI by comparison, is evolving at lightspeed. Experts have designed, tweaked, and iterated upon every detail end-to-end, to ensure the highest efficiency, safety, and quality standards that meet strict industry requirements and tight delivery schedules.
Manufacturing production lines have been meticulously optimized and perfected for decades. Learn more in this guest post from Cross Compass by Romain Angénieux, AI Simulation Group Leader Steven Weigh, Global Brand Identity Designer and Antoine Pasquali, Chief Technology Officer.ĭesigning and deploying cutting-edge AI solutions for manufacturing environments is a complicated process.