Siemens’ Post

Very helpful

Appreciate thinking and highlighting the rapid pace of technological advancement. The reference to robots processing products that have yet to be conceived suggests that we're moving towards a future where AI and automation are not just supporting existing processes but actively driving innovation in product design and manufacturing. Digital twins, which are virtual representations of physical assets, combined with AI, will allow for more efficient and precise simulations of products before they even exist. This could significantly reduce time to market, improve product quality, and foster the development of entirely new categories of products that we can only imagine today.

I find the use of AI and synthetic data in robot training truly transformative. It was informative to read how by simulating real-world conditions, robots can learn faster, cutting down on costly physical testing. Digital twins take this a step further, allowing for scalable and adaptable training. While I'm still exploring these topics, I'm curious—what do you think the role of human oversight will look like as these technologies are implemented at scale? How do we ensure robots are being trained safely and ethically? Would love to hear your thoughts!

Emulation wasn't even heard of by most in the Material Handling Industry 10 years ago. If you aren't doing digital twin or Emulation by now you are very far behind.

AI mechatronics Field products scanners and collect data in binary code then decode. Matching with human reports about products, given to the LMM model with history helping modify digital and analogue design products.

Very informative

Definitely worth reading and keep Growing Siemens

Training robots in virtual environments is a great example of AI driving efficiency while keeping humans at the center of innovation.

Informative

The integration of AI, simulation, and digital twins in robot training is a significant breakthrough. By leveraging these technologies, robots can learn and adapt at an unprecedented pace, bridging the gap between theory and practice through the Sim2Real approach. This method accelerates training, reduces costs, and enhances efficiency. The concept of "robot schools" in the industrial metaverse is noteworthy. These virtual environments allow robots to practice tasks and develop problem-solving skills in a highly realistic setting. This approach enables robots to acquire complex skills quickly, making them invaluable assets in diverse industries. The future of automation looks promising, with robots poised to collaborate safely with humans. Moreover, the use of synthetic data and digital twins addresses data scarcity issues in AI training. By simulating real-world conditions, robots can be prepared for various scenarios, ensuring adaptability and responsiveness. This technology is transforming robot training and redefining industrial automation and sustainability. As we move forward, AI and digital twins will play a pivotal role in shaping the future of manufacturing. 🙏