AWS Robotics Blog

Join AWS Robotics experts and innovative robotics companies at re:Invent this year to learn how to advance and speed up efforts to build robotics applications. 

Amazon Web Services (AWS) Robotics has released an introductory Cloud Robotics Curriculum designed to help students, educators, and entry level developers build robotics applications with Robot Operating System (ROS) and AWS RoboMaker.

In this blog, learn about AWS RoboMaker WorldForge, and how 3D world generation can be used to streamline testing and training of core robotic algorithms at scale.

In this blog, we walk you through the steps required to train a machine learning model using RL on AWS RoboMaker. Specifically, we familiarize you with the development environment, then define the steps required to complete RL machine learning within AWS RoboMaker.

Nowadays, risk of spreading disease is a key concern in hospitals, where doctors, nurses and other caregivers are on the front lines helping patients. Hospitals have started using robots in daily operations such as contactless delivery and room disinfection to reduce risk of spreading disease. As the need for robots in healthcare grows, better tools are needed to build, test, and deploy robotics applications quickly and safely. Amazon Web Services (AWS) has developed a Gazebo simulated Hospital World and published it as open source so that robotics companies within the Healthcare industry can more easily test their robots in a simulated hospital environment. In this blog, I will provide an overview of the hospital and share my experience using it to test a PR2 robot, including software failures that I encountered that you may find helpful.

Introduction Alexa is Amazon’s cloud-based voice service and Alexa-compatible devices allow users to control their smart devices with voice commands. Today, Alexa is available on hundreds of millions of devices from Amazon echo devices to third-party devices. Alexa Skill Kit (ASK) provides a way for robot developers and manufacturers to build a natural voice interface for their […]

Earlier this year, we held the AWS JPL Open Source Rover Challenge, a four-month competition where participants from around the world used deep reinforcement learning to drive digital robot models on a virtual Mars landscape. Participants created autonomous navigation models for the robot and trained them in AWS RoboMaker simulation. The virtual robot used in […]

The S3 rosbag cloud extension enables customers to easily configure and record data from robots as rosbags, and upload them to Amazon S3, which they can later use to analyze events, troubleshoot existing applications, and provide as inputs to AWS RoboMaker log-based simulation for regression. In this blog, we will review three ROS nodes in the S3 rosbag cloud extension that create and upload rosbag files from the robot to Amazon S3, with hands-on examples of how to use the nodes.

This blog will explore best practices for testing and validation of robotics and autonomous systems running Robot Operating System (ROS) software. We will review how developers can increase their feature velocity and reduce errors by using simulation-based testing.

The highly anticipated Robot Operating System 2 distribution Foxy Fitzroy (ROS 2 Foxy) has been released. ROS 2 Foxy is the most secure and reliable ROS distribution to date for production robotics application development. In this blog, we highlight key features and improvements available in this new release.