S U M M I T - Amazon Web Services...Video Streams, Amazon Rekognition, and Amazon CloudWatch LEX...
Transcript of S U M M I T - Amazon Web Services...Video Streams, Amazon Rekognition, and Amazon CloudWatch LEX...
S U MM I TB E R L I N
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.SUMMIT
Rise of Robotics:Innovating the Future with IoT & AI
Özkan Can, AWS - Senior Solutions Architect
@_ozzc
A W S S U M M I T B e r l i n
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.SUMMIT
Agenda
• Cloud Robotics
• Robot Development
• Robot Collaboration
• Reinforcement Learning
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Robotics trendsin 2018
Robotics is undergoing fundamental change in collaboration, autonomous
mobility, and increasing intelligence
Source: IDTechEx
• Logistics
• Construction
• Retail
• Hospitality
• Healthcare
Robots are being put to work every
day across many industries
• Agriculture
• Energy Management
• Oil and Gas
• Facilities Management
• Household chores
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
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Mobile roboticstrends 2018
We are at the beginning of an inflection point, where expected growth in the use of mobile robots will increase by almost tenfold over the next 2–3 years, but…
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Cloud Robotics
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Robotic development is difficult and time consuming
Requires machine learning expertise for intelligent functions
Many prototyping iterations
Days spent setting up and configuring
Months to building a realistic simulation environment
Duplicated efforts integrating an application management system
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.SUMMIT
Robotic development cycle
2) Develop robotics
application
1) Select robotics software
framework
4) Deploy and manage
application
3) Test and simulate
application
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AWS RoboMaker
A service that makes it easy for developers to develop, test, and deploy robotics applications, as well as build intelligent robotics functions using cloud services
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AWS RoboMaker service suite
Development Environment
Simulation Cloud Extensions for ROS Fleet Management
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Robot Operationg System (ROS) Primer
Robot Hardware
OS
ROS
Robot Application
ROS is an open-source,
meta-operating system for your robot.
https://wiki.ros.org
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ROS Nodes
ROSMaster
ROSNodes
/topicsROS
NodesROS
NodesROS
Nodes
ROSNodes
ROSNodes
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ROS Nodes
ROSMaster
ROSNodes
/topicsROS
NodesROS
NodesROS
Nodes
ROSNodes
ROSNodes
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Hello World Node Publisher
1 import rospy
2 from std_msgs.msg import String
3 pub = rospy.Publisher('chatter', String, queue_size=10)
4 rospy.init_node('talker', anonymous=True)
5 rate = rospy.Rate(10) # 10hz
6 while not rospy.is_shutdown():
7 hello_str = "hello world %s" % rospy.get_time()
8 pub.publish(hello_str)
9 rate.sleep()
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Hello World Node Subscriber
1 import rospy
2 from std_msgs.msg import String
3 def callback(data):
4 rospy.loginfo(rospy.get_caller_id() + "I heard %s", data.data)
5 rospy.init_node('listener', anonymous=True)
6 rospy.Subscriber("chatter", String, callback)
7 rospy.spin()
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AWS RoboMaker Investment in ROS
Actively guiding the development of the newest version of ROS (ROS2) via representation on the Technical Steering Committee (TSC) of OSRF
Investment to provide Long Term Support (LTS) for ROS2
• LTS maintains backwards compatibility
• LTS allows developers to release commercial and production-level products on ROS2
Investment to increase ROS2 packages
• Packages are reusable and provide unique robotics functions such as mapping and navigation
• AWS driving migration of 113 critical packages identified for ground mobility robots to ROS2 by the first ROS2 LTS release
Leading the security and logging design of ROS2 and contribution to the ROS2 core messaging system, Data Distribution Service (DDS)
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AWS RoboMaker Architecture
ROS/ROS2
DevelopmentEnvironment
SimulationFleet
ManagementCloud Extensions
for ROS
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AWS RoboMaker
Cloud Extensions for ROS
Cloud extensions written as ROS packages automatically create connections and make API calls to AWS services, such as Amazon Lex, Amazon Polly, Amazon Kinesis Video Streams, Amazon Rekognition, and Amazon CloudWatch
LEXspeech
recognition
POLLY
speech
generation
KINESIS
VIDEO
STREAMS
video
streams
REKOGNITION
image and video
analysis
CLOUDWATCH
logging and
monitoring
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Amazon CloudWatch extension in operation
ROS Nodes
and topics
AWS Cloud
Node logic
Temporary
security
credential
Stored
credential
Robot
… or …
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Collaboration through IoT Services
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To securely connect devices to the AWS cloud & other devices at scale
To fully integrate with other AWS services to reason
on top of the data (Analytics, Databases, AI, etc.)
To route, process, and act upon data from connected devices
AWS IoT Core is a managed service that lets connected devices easily
and securely interact with cloud applications and other devices.
To enable applications to interact with devices even
when they are offline
AWS IoT Core
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Edge Cloud
Law of Economics
Law of Physics
Law of the Land
AWS IoT Greengrass
AWS IoT Greengrass extends AWS IoT onto your devices, so that they can act
locally on the data they generate, while still taking advantage of the cloud.
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AWS IoT ROS Extension
https://github.com/aws-robotics/aws-iot-bridge-example
AWS IoT CoreAWS IoT Greengrass
Edge
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Fleet Management
AWS IoT CoreAWS IoT Greengrass
Edge
AWS RoboMaker
Deploy
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ArtificialIntelligence
MachineLearning
ReinforcementLearning
SupervisedLearning
UnsupervisedLearning
Reinforcement learning in the broader AI context
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Machine Learning Overview
SUPERVISED UNSUPERVISED REINFORCEMENT
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Reinforcement Learning in the real world
Reward positive behavior
Don’t reward negative behavior
The result!
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Reinforcement Learning use cases
AUTONOMOUS CARS FINANCIAL TRADING DATACENTER COOLINGFLEET LOGISTICS
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What is Reinforcement Learning?
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How does learning happen?VALUE FUNCTION
POLICY FUNCTION
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RL Algorithms – Value Approximation
CHALLENGE Explore all (state, action, outcome) combinations in a real world race to create the value function
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RL Algorithms – Value Approximation
CHALLENGE Explore all (state, action, outcome) combinations in a real world race to create the value function.
VERDICT Not possible
SOLUTION Approximate the value function using supervised learning. Use data from exploration to create a model capable of estimating the likely cumulative reward for all possible actions in a state.
The action with the highest cumulative reward,
is the best action.
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RL Algorithms – Policy Approximation
ALTERNATIVE Try to directly map actions to states without using a value function.
HOW Use supervised learning to train a policy function model that returns the best action for any state.
How do we know this is a good action? Because it gave the highest reward. Keep improving our model in the direction of the highest reward.
FAMOUS EXAMPLE
Dota2 Challenge - OpenAI Five
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Reinforcement Learning Algorithms Compared
Value Approximation Policy Approximation
AdvantagesMore stable performance when it works, and tends to
converge on global optimum
Effective in continuous action spaces, can learn stochastic policies,
and faster convergence
DisadvantagesDifficult to converge if too many (state, action)
combinations, slower convergence in general, and
can’t learn stochastic properties
Typically converges to a local rather than global optimum, high
variance in estimating the gradient adversely affects stability, and
evaluating a policy is generally inefficient
Examples Q-Learning, Deep Q Network, Deep Double Q Network Policy Gradient, Proximal Policy Optimization (PPO)
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Amazon SageMaker
Amazon SageMaker is a fully-managed servicethat covers the entire machine learningworkflow to label and prepare your data, choose an algorithm, train the algorithm, tune and optimize it for deployment, makepredictions, and take action.
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Amazon SageMaker RL
Amazon SageMaker RL builds on top ofAmazon SageMaker, adding pre-packaged RL toolkits and making it easy to integrate anysimulation environment.
• Intel Coach
• Ray RLLib
• TensorFlow,…
• Support for Open Source and
Commercial Environments
Pre-Packaged RL Toolkits
• OpenAI Gym
• MXNet
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.SUMMIT
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AWS Cloud
NAT
gateway
VPC
Models
Simulation
video
Metrics
Simulation Architecture
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Gazebo
Gazebo is an open-source,
Robot simulation tool.
https://gazebosim.org
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Interactions with Simulator
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In Closing
Accelerate development time
with zero infrastructure provisioning
through AWS services for
Cloud Robotics.
• AWS Robomaker
• AWS IoT Core
• AWS IoT Greengrass
• Amazon SageMaker RL
Cloud Robotics on AWS
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.SUMMIT
GO BUILD!
Özkan Can, AWS - Senior Solutions Architect
@_ozzc
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