Three artificial intelligence and robotics trends

Artificial intelligence (AI) and robotics can create a potent combination. AI has the ability to make robots smart, learn quicker, and use the cloud to reduce information load on physical servers.

03/23/2018


The combination of artificial intelligence (AI) and industrial or collaborative robotics has the potential to change the world. AI unlocks entirely new capabilities for robots, which, without AI, are rigid and unresponsive to the world around them.

The potential for disruption in the industrial sector is high. Despite the fact that industrial processes are already highly automated, there are still plenty of ways in which industrial robots can be improved with the addition of AI.

Types of industrial and collaborative robotic learning

A robot's ability to learn is directly related to its overall capabilities. The three main types of robotic learning involve supervised learning, unsupervised learning and reinforcement learning. Each varies in complexity, but the purpose is the same in all three learning methods.

Supervised learning is pattern recognition; feeding a robot data that it is then supposed to learn whatever pattern is intended by the instructors. Unsupervised learning doesn't involve any specific task, it simply involves feeding a robot massive amounts of data, hoping it will start to understand the world around it. Reinforcement learning involves giving a robot or system a goal and allowing it to learn how to reach that goal.

Three trends to watch in AI and robotics

While there are many futuristic scenarios with AI and robotics technology, there are a few present-day applications to keep an eye on. These three trends will be major factors in the development of AI and robotics technology.

1. Robot training: AI is making robots easier to train, which in turn makes them a more viable investment for smaller companies as it reduces the cost of installation, training and ongoing programming. Robots can be trained by simply guiding their arms a few times - it learns through demonstration and programs the correct motion itself. The easier it is to teach a robot new things, the more it can learn.

Courtesy: Bob Vavra, CFE Media2. 3-D vision: Even the simplest tasks a robot performs will depend on 3-D machine vision to feed data into AI technology. Grasping an object, for example, without pre-determined locations and motions would be impossible without machine vision capable of reconstructing a 3-D image, and AI to translate this visual information into a successful action on the part of the robot.

3. Cloud robotics: Robotic deep learning using image classification and speech recognition often relies on huge datasets with millions of examples. AI requires more data than can realistically reside on most local systems. In this way, advances in cloud robotics are necessary for the advancement of AI and robotics technologies. Cloud robotics allows intelligence to be shared across all robots in a connected environment.

AI has radical potential when it comes to changing the way robotics technology operates inside and outside of factories across the world. While AI is still in its infancy, it's likely to disrupt, and improve, the way robots operate.

This article originally appeared on the Robotics Online Blog. Robotic Industries Association (RIA) is a part of the Association for Advancing Automation (A3), a CFE Media content partner. Edited by Chris Vavra, production editor, Control Engineering, CFE Media, cvavra@cfemedia.com.



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