Robotics often involves training complex sequences of behaviors. Reinforcement learning (RL) is an emerging machine learning (ML) technique that can help develop solutions for exactly these kinds of problems. It learns complex behaviors without requiring any labeled training data, and can make short-term decisions while optimizing for a long-term goal.
Woodside and AWS engaged Max Kelsen to assist in the development and open-sourcing of the RoboMaker and RLEstimator components. Woodside Energy uses AWS RoboMaker with Amazon SageMaker Kubeflow operators to train, tune, and deploy reinforcement learning agents to their robots to perform manipulation tasks that are repetitive or dangerous. The framework developed by Max Kelsen allows the team to iterate and deploy at scale.
The task of stitching together machine learning workflows for robotics using Amazon SageMaker and AWS RoboMaker is non-trivial, consuming valuable time for both data scientists and roboticists.
This project saw the Max Kelsen team develop Amazon SageMaker RL Components for Kubernetes, which you can use in your Kubeflow pipelines to invoke and parallelize SageMaker training jobs and AWS RoboMaker simulation jobs as steps in your RL training workflow, without having to worry about how it runs under the hood.
SageMaker Components in your Kubeflow pipeline simply loads the components and describes your pipeline using the Kubeflow Pipelines SDK. SageMaker RL uses open-source libraries such as Anyscale’s Ray to start training an RL agent by collecting experience from Gazebo (an open-source software to simulate populations of robots in complex indoor and outdoor environments) in AWS RoboMaker using ROS (a set of software libraries and tools that help you build robot applications).
When the training is completed, the RL agent model is stored in an Amazon Simple Storage Service (Amazon S3) bucket, and an Amazon SageMaker inference node can be created for deployment in production. You can then download the model to the robot with the same ROS structure from the simulation to perform the required tasks.
This project allowed AWS to launch Amazon SageMaker Reinforcement Learning Kubeflow Components supporting AWS RoboMaker, a cloud robotics service, for orchestrating robotics ML workflows.
Orchestrating robotics operations to train, simulate, and deploy RL applications is difficult and time-consuming. Now, with SageMaker RL components and pipelines, it’s faster to experiment and manage robotics ML workflows from perception to controls and optimization, and create end-to-end solutions without having to rebuild each time.
You can read in more detail about how these components can be used within an end-to-end simulation and training pipeline by reading the post on AWS’s technical blog, and find the components within the Kubeflow github repository.
"Our team and our partners wanted to start exploring using machine learning methods for robotics manipulation, but before we could do this effectively, we needed a framework that would allow us to train, test, tune, and deploy these models efficiently. Utilizing Kubeflow components and pipelines with SageMaker and RoboMaker provides us with this framework and we are excited to have our roboticists and data scientists focus their efforts and time on algorithms and implementation."
– Kyle Saltmarsh, Robotics Engineer at Woodside Energy