Robots are a major part of a variety of industries. Innovative companies use them to solve complex problems in manufacturing and supply chain logistics, improving productivity and enhancing worker safety. Integrating artificial intelligence with robots is a challenge that requires software, and reinforcement learning technology offers a powerful approach for the task.
Reinforcement learning focuses on teaching algorithms to learn through trial and error to optimize its performance within a given system, much like a game player or robot. The difference is that the goal is not just to gain a certain level of skill but to understand and react to changes in the system as a whole.
A robotics startup called Brickbot is using this type of AI to teach its robots how to build with LEGO bricks. The robots learn through sensor data from their environment and adapt to the conditions by modifying their behavior. Brickbot can then be used in a warehouse to navigate its space, supply product parts, perform quality inspections, and automate the entire process without human intervention.
Other machine learning technologies can be used to make robots smarter, but reinforcement learning is one of the most promising approaches. Its ability to teach machines to respond to changing environmental conditions and to take actions that maximize their performance is impressive. This is a powerful technique that can lead to breakthroughs in AI, such as the accomplishments of Boston Dynamics with its robots that are able to traverse challenging terrain. This coupled with deep learning could eventually result in a machine that behaves more along the lines of human creativity—and Google sibling company DeepMind has already made significant progress toward this goal with its program AlphaGo, which beat a world-class Go player in 2016.
