3. What makes a robot?
Perceive Plan Act
… and repeat
4. Robotics use is accelerating in key industries
Robotics is undergoing fundamental
changes in collaboration, autonomous
mobility, and increasing intelligence
Source: IDTechEx
By 2023, it’s estimated that mobile autonomous robots will
emerge as the standard for logistic and fulfillment processes
By 2030, 70% of all mobile material
handling equipment will be autonomous
Logistics Construction
Consumer Home Energy and Utilities
Retail Healthcare
Oil and Gas Agriculture
6. Design and develop robotics applications
and functionality
Agile development of robotics application requires
software reuse and iterative development
7. AWS contributions to ROS 2
https://github.com/aws-robotics
Quality of service (QoS)
features for topics
ROS 2 launch
sandboxing extension
Cross-compilation
tools
Secure-ROS 2 (SROS2)
improvements
Created and maintains
rcpputils core package
Runtime analysis tools
address and thread
sanitizers
(Asan/Tsan)
10. Challenges facing robotics developers
Lack of virtual assets
for simulation
High cost of
simulations at scale
Lack of infrastructure to
run simulations at scale
Lack of infrastructure for
deployment at scale
Lack of security for
deployment and update
11. AWS
RoboMaker
Easily simulate
and deploy robotics
applications at cloud scale
Fleet
management
Test and
verify
Deploy
and
update
Design
and
develop
Cloud-based
simulation
Cloud-based
simulation
12. Cloud-scale simulations
Fully managed simulation infrastructure at cloud scale
with pay-as-you-go pricing
Multi-robot simulations for
testing fleet operations
Regression testing at cloud
scale with CI/CD integration
Machine learning
model training
13. Use pre-built virtual 3D worlds provided out
of the box, or bring your own
Automatically provisioned, configured, and
managed cloud infrastructure for Gazebo
simulator
Automatically scale based on simulation
complexity
Resource-based pay-as-you-go pricing at a
minute granularity
Cloud-scale
simulations
14. Cloud-scale simulations
Log files
Log files 1
+ Simulation
world 1
Robot
application
Simulation
job
+Robot
application
Simulation
world 2
Simulation
job
+Robot
application
Simulation
world N
Simulation
job
Log files 2
Log files N
Metrics
15. Regression testing upon every code update and
every software release
Playing back recorded rosbags or running Gazebo-
based simulations
Large-scale and concurrent simulations triggered in
a batch using AWS RoboMaker simulation APIs
Integration with CI pipeline (Jenkins, Travis, AWS
CodePipeline, etc.)
Simulation
use case 1
Regression testing at
cloud scale with CI/CD
integration
16. Problem
• Limited test coverage for different floor layouts and scenarios
• Costly and time-consuming to test
• Late bug discovery in the field
Solution
• iRobot built a CI/CD pipeline for large-scale and automated
testing using the AWS RoboMaker simulation service
• More than 40 automated tests on each code commit and more
than 500 automated tests for each release candidate
Business benefits
Much faster testing and release cycle (1 hour vs. 3 weeks for testing
70 complex localization scenarios)
Case study: Regression testing
17. Use case 1: Regression testing with CI/CD integration
AWS CodePipeline
AWS CodeBuild
Build and bundle
code in ROS container
Bundles stored in
Amazon S3
AWS RoboMaker
Create robot and
simulation application
AWS RoboMaker
Launch simulations
via AWS Lambda
AWS RoboMaker
Aggregate results
from simulations
Branch: Integration
Git repository
Clone on git hook (merge)
AWS RoboMaker
Simulation status
check via AWS
Lambda
If test passed,
merge with master
Branch: Master (release)
1
2
3
4
5
18. Simulate multiple robots within the same environment
Connect multiple simulations to a central fleet
management software to test multi-robot orchestration
Simulate inter-robot interactions or missions across robots
Simulation
use case 2
Multi-robot
simulations for testing
fleet operations
19. Problem
• Bastian’s software solutions enable the orchestration of a
fleet of robots
• Software testing currently requires physical robots;
practical limitation – can test only 8–10 robots in the lab
Solution
• AWS-enabled simulation of a multi-robot environment
with 35+ robots, thus enabling testing without physical
robots
• AWS services used: AWS RoboMaker, AWS Lambda
Business benefits
Bastian Solutions is easily able to test applications for larger
environments without having to stand up physical devices
Case study 2: Multi-robot
simulations
20. Rapidly generate trial data in simulation to train
reinforcement learning model
Train reinforcement learning model natively in the
simulation or in Amazon SageMaker
Run concurrent simulations to speed up training of a
single model
Simulation
use case 3
Machine learning
model training
21. Use case 3: Machine
learning model training
Use simulation to generate training data and
test trained AI/ML models in simulation
Reinforcement learning (RL) can be used to
learn a control scheme in simulation
Amazon
SageMaker
training
Simulation
environment
Actions
Observation
Trained
model
RL
agent
Model updates
Training data
Observation
Action
Reward
Deploy
model
Fleet management deployment
22. Deploy and update at cloud scale
Ability to handle
large fleet sizes
Fully managed over-the-air update infrastructure at cloud scale
Organization of robots
by logical fleet
Built-in
security features
23. Register robots with AWS RoboMaker fleet
management, and organize them into fleets
Deploy a ROS application into a robot fleet securely
with just a few clicks
Conditional over-the-air (OTA) updates
Fleet monitoring and alerting*
Fleet deployment auto-rollback*
* Coming soon
Cloud-scale
fleet
management
24. Resources
Tutorials and workshops
Developer guide
Try AWS RoboMaker today
Regression testing at cloud scale with
CI/CD integration
Multi-robot simulations for testing
fleet operations
Machine learning model training
in simulations
Over-the-air deployment with cloud-scale
fleet management
aws.amazon.com/robomaker
Blog
Title: Simulate and deploy robotics applications at cloud scale
Target audience: Customers building robotics, including BDMs, DevOps, Developers, and Ops teams of robotics companies or partners and enterprises looking at robotics use cases
Services covered: Primary: AWS RoboMaker, Secondary: AWS IoT, AWS IoT Greengrass, ML/Amazon SageMaker
Description
The business case for robotics and automation has never been more attractive. Enterprise adoption is increasing and consumer behavior is evolving, raising the demand for robotics of all types to assist with logistics, material movement, delivery, and equipment monitoring as well as the use of robots to simplify everyday tasks. Learn about the role that cloud services will play in shaping the future of robotics by allowing developers to partition functionality between their physical robot and the cloud, enhance the security of mission critical functionality, and enable the adoption of CI/CD pipeline practices to enhance the quality and speed of robotic app development. This block explains how customers are adopting AWS services to enable rapid testing through simulation, enhance robot functionality through cloud AI services, manage multi-robot fleets in production, and garner edge and performance data to provide valuable business insights.
Resources for presenters: https://wisdom.corp.amazon.com/Pages/Solutions_Robotics.aspx
BD: Vaibhav Phadnis, vphadnis@amazon.com
Product Marketing: Dylan Locsin, dlocsin@amazon.com
Product Management: Ray Zhu, rayzhu@amazon.com
So what is a robot?
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Robots are like networked IoT devices – EXCEPT they also ACT and INTERACT with their environments, physically to do things for us>
Robotics software has three broad components:
Sense & Perceive the environment, Plan the actions, and Act on the plan. Within these, the planning and decision making is critical to add autonomy and intelligence to robotics applications.
There are specific industries we see robotics playing an important role in. This includes logistics, construction, retail, hospitality, healthcare, agriculture, energy, oil and gas, facilities, and consumer home products. Each of these applications is demanding more autonomy and more intelligence from the robots operating in them.
If you think about a robot in the home, we expect it to be social – to recognize us, talk to us, and interact with us.
Or in a warehouse facility, we expect the robot to navigate and detect objects as its moving around the warehouse floor so it can effectively pick orders and move inventory.
Let’s cover some basics: ROS – Robot Operating System. Most widely used software framework for teaching and learning about robotics – over 16 million .deb (Linux Debian) packages downloaded in 2018, a 400% increase since 2014. It’s actually not an OS, but is a middleware layer built to enable robotics applications
Founded in Stanford labs over 10 year ago, now managed by the Open Source Robotics Foundation (OSRF)
Global open-source community supports two products—Robot Operating System (ROS) and Gazebo
Gazebo < DEFINITION adapted from their website >
Robot simulation is essential for robotics development. Good simulation makes it possible to rapidly test algorithms, design robots, perform regression testing, and train AI system using realistic scenarios. Growing out of roots at the University of Southern California in 2002, Gazebo now offers the ability to accurately and efficiently simulate populations of robots in complex indoor and outdoor environments.
It has a robust physics engine, high-quality graphics, and convenient programmatic and graphical interfaces. The OSRF continues development of Gazebo with support from a diverse, vibrant open source community.
AWS is active in the open source communities, with our customers in the robotics space, especially ROS, and new ROS 2. Here are some things we’ve contributed to improve, harden and get ROS2 ready for production commercial usage.
Cross-Compilation tools allows users to easily compile ROS2 for their target boards, from a build machine with a different architecture
SROS2- Policy generation for securing nodes, generate keys for node communication and verification.
Launch Sanboxing allows you launch nodes inside containers to monitor and restrict resource usage, think of micro services “cloud” techniques for ROS
In ROS2 –D we solved: 4 memory leaks impacting production, 17 memory leaks impacting test and 2 data races impacting Fast RTPS
Improved QA to detect memory and concurrency bugs
ARMHF Support
Rcpputils contains things like thread safety annocation macros, library discovery as well file system and type triats helpers
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But developing robots with that level of autonomy and intelligence is difficult and time consuming.
First, it requires machine learning expertise, as well as significant background in mechanical and software engineering. Most roboticists spend long periods of time prototyping as they evaluate all the different components necessary for autonomous functionality, including simulating cameras, sensors, and actuators on a physical dev kit while they optimize for both functionality and cost..
Due to the complexity of platforms, before a developer can start writing application code, they often have to spend hours or days to ensure the development environment is set up properly. They then have to download the robot OS software framework, different kinds of tooling, and ensure all those elements are working together properly.
Additionally, not all developers working on robotic applications have access to robot hardware as this can be extremely costly and typically what is being used for prototyping is never the same as what goes to commercial production.
Finally, there are many challenges with managing growing fleets and application updates for robots in the field.
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Highlight design and develop. ML model training is at design and develop stage. Simulation is not only for testing
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<What is Gazebo vs. what is RoboMaker>
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ROS and ROS 2.0 configured in the cloud
Simulation service for parameter tuning
Batch Simulation and CI/CD pipeline for regression testing
Fleet Management provide over the air update capabilities to a robotic fleet.
Cloud Extensions easily interface ROS with AWS services such as Amazon Kinesis Video Streams, Amazon Rekognition, and Amazon CloudWatch.