Whether robots are improving the efficiency of industries, helping with medical surgeries or enabling Earth and space exploration, these devices are increasingly attracting the attention of enterprises and their software developers.
For companies interested in further developing these devices, Amazon Web Services Inc. is boosting its RoboMaker — its cloud solution for robotic developers — with new industrial machine learning services, announced this week during the virtual AWS re:Invent event.
“We continue to see the story of how processing is moving to the edge and cloud services are augmenting that processing at the edge with unique and new services,” said Roger Barga (pictured), general manager of AWS robotics and autonomous services at AWS. “Andy [Jassy, chief executive of AWS] talked about five new industrial machine learning services yesterday, which are very relevant to exactly what we’re trying to do with AWS RoboMaker.”
Barga spoke with John Furrier, host of theCUBE, SiliconANGLE Media’s livestreaming studio, during AWS re:Invent. They discussed how new ML services improve the AWS RoboMaker, the importance of testing in simulation to enable dynamic robots, and some interesting use cases. (* Disclosure below.)
Testing in simulation makes dynamic robots possible
Amazon Monitron, an end-to-end system that uses ML to detect abnormal behavior in industrial machinery, allowing predictive maintenance and reducing unplanned downtime, is one of the new services furthering RoboMaker’s capabilities.
“It’s a whole solution from an edge device to a gateway to a service,” Barga said.
For newer machines that come with their own sensors, and therefore don’t need the full Monitron service, it is possible to instead use Amazon Lookout for Equipment. This gives customers a way to send their sensor data to AWS and use pre-built machine learning models to detect any abnormal machine behavior.
“With AWS RoboMaker, we have ROS cloud service extensions, which allow developers to connect their robot to these services, and so increasing that combination of being able to put sensors and processing at the edge, connecting it back with the cloud, where you can do intelligent processing, and understand what’s going on out in the environment,” Barga explained.
AWS is constantly improving the capabilities of RoboMaker to handle the wide variety of business cases and the fast pace of their evolution. “No single service is going to solve all the problems,” Barga pointed out. “We give customers these basic building blocks, but we also think about working with the customer backwards. What is the finished solution that we could give a customer that just works out of the box?”
As business questions change all the time, developers often need to reprogram a robot on the fly so that it can adapt to new demands. Therefore, an important feature is testing in simulation.
“This is a dialogue we’ve been seeing coming up over the last year, where roboticists are starting to educate their company that a robot is a device that can be dynamically programmed at any point in time,” Barga said. “They can test their application and simulation while the robots are out in the field, verify it’s going to work correctly in simulation, and then change the mission for that robot dynamically.”
Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of AWS re:Invent. (* Disclosure: Amazon Web Services Inc. sponsored this segment of theCUBE. Neither AWS nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
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