How Machine Learning is Used in Collaborative and Industrial Robots
Machine learning is advancing the capabilities of collaborative and industrial robots. Without 3D sensors or deep neural networks, robots are blind and one-dimensional – they’re restricted to one repetitive task that’s been preprogrammed with no ability to account for variables in their environment. This inherently limits the productivity potential of robots.
Now, with vision sensors and machine learning capabilities, collaborative and industrial robots are able to achieve far more than they ever could on their own.
What's Possible with Robots and Machine Learning?
A recent application of machine learning in robotics comes from UC Berkeley and Siemens with their DexNet 2.0 robotic system, developed last year, to pick up parts that it had never seen before. Training a robot to grasp objects without dropping them requires quite a bit of programming, practice, and trial and error.
This new robotic system, leveraging a 3D sensor and deep-learning neural network which processes information on the shape and appearance of an object, as well as how to grab it. The robotic system is 98% accurate when it is at least 50% confident it could grab a new object. If it was less than 50% confident, the system would perform a quick visual inspection, and then grab the part with 99% accuracy.
This is an entirely new capability for robots and could transform the way material handling robots are deployed and programmed in commercial applications.
What Types of Machine Learning are Being Used in Robotics?
There are different types of machine learning in industrial and collaborative robotics. The example above is an advanced version of computer vision or robot vision. Essentially, complex optical equipment for image capture feeds neural networks so that a robot can “see.” In most instances, this translates into robotic guidance to avoid collision, seam tracking during welding, and to ensure parts are grasped correctly.
Another fascinating new type of machine learning in robotics is imitation learning. Essentially, in this scenario a robot can be programmed by demonstrating how to complete a task. For example, someone could show a collaborative robot how to grasp an object by guiding the robotic arm the first few times. In this way, the robot would learn to grasp the object on its own.
There are other types of machine learning in robotics, such as self-supervised learning or multi-agent learning, but imitation learning and computer vision are two of the main methods.
Machine learning opens up entirely new possibilities for industrial and collaborative robot applications, allowing both types of robots to perform tasks that were previously impossible. Machine learning will have a major impact on robotic capabilities and will likely become a fixture in all robotic systems one day.
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