Those who follow robotics development have been noticing a quiet revolution in the area for some time. While self-driving vehicles have received a lot of attention, research on the convergence of AI, machine vision, and machine learning is quickly becoming the basis for the next phase of robotics.
Roboticists are enabling a wide range of new possibilities by integrating machine vision and learning capabilities, such as vision-based drones, robotic harvesting, robotic sorting in recycling, and warehousing pick and place. We’ve now reached the tipping point: the point at which these applications are good enough to deliver substantial benefit in semi-structured situations where traditional robots would fail.
We had physically powerful robots limited to big industries and manufacturing units to perform hazardous tasks that are a bit risky for human labor to do. They are good at performing boring repetitive tasks. They are a valuable asset for such industries, but incapable in front of intelligent robots.
A silent revolution is taking place in the field of artificial intelligence (AI) robotics. AI robots are loaded with advanced AI models and vision. They can watch, learn, and respond to make the best option given the situation.
Popular robotics coverage focuses on home-butler-style robots and self-driving automobiles since they are highly related to our daily lives. Meanwhile, AI Robotics is taking off in less visible but vital aspects of our environment, including e-commerce fulfilment centers and warehouses, farms, hospitals, and recycling facilities. All sectors have a significant influence on our lives, but not activities that the typical person sees or interacts with on a regular basis.
Traditional robotic automation depended on ingenious engineering to make pre-programmed-motion robots useful. That may have worked in the automotive and electronics industries, but it is ultimately very limiting.
Giving robots the ability to see radically alters what is conceivable. Computer Vision, the branch of AI concerned with teaching computers and robots to see, has seen a night-and-day shift in the last 5-10 years, courtesy of Deep Learning. Deep Learning teaches massive neural networks (based on examples) to recognize patterns; in this case, pattern recognition allows comprehension of what’s where in photos. Deep Learning, on the other hand, provides skills that go beyond sight. It enables robots to learn what actions to perform in order to finish a task.
Indeed, every robotic deployment necessitates the convergence of many excellent components and a staff that understands how to make them all function together. Aside from AI, there is the long-standing technology of dependable industrial-grade manipulator robots. And, most importantly, there are cameras and computers that are constantly improving and getting more affordable.
The extent to which robots contribute to our daily lives, frequently without our even realizing it. Indeed, we are unlikely to see robots directly engaging with the products we use every day, but there will come a day when the majority of the products in our home have been touched by a robot at least once before reaching us.
