The new ROS 2 AI Integration Working Group is focused on enabling Machine Learning technologies for ROS 2. In this presentation you'll find: - ALMA: the Human Centric Algebraic Machine Learning project - SustainML - Enabling ML technologies for ROS 2 robots with Vulcanexus

1. AI Integration WG
ALMA, SustainML & ROS 2 use case
28/06/2022

2. Agenda
02 Application Aware, Life-Cycle Oriented Model-HW Co-Design
Framework for Sustainable, Energy Efficient ML Systems
SustainML
01 Human Centric Algebraic Machine Learning
ALMA
03 Serving models to ROS 2 robots, with Vulcanexus
environment
Enabling ML technologies for ROS 2

3. Agenda
02 Application Aware, Life-Cycle Oriented Model-HW Co-Design
Framework for Sustainable, Energy Efficient ML Systems
SustainML
01 Human Centric Algebraic Machine Learning
ALMA
03 Serving models to ROS 2 robots, with Vulcanexus
environment
Enabling ML technologies for ROS 2

4. ALMA Mission
To provide a new ML paradigm, known as
AML
● Easily understandable by users (no black-box)
● Ease of interaction (Human-AML interaction)
● Seamless integration with AML-IP
● Ensure long-term maintenance of AML environments
Spain
Portugal
Germany
France Finland

5. What is AML?
Symbolic AI:
● Description of the world using formulas
● Difficulty learning from data
● High transparency
Statistical Learning (including Neural Networks):
● Learning from data
● Difficulty using formal descriptions
● Usually opaque
Algebraic Machine Learning:
● Description of the world using formulas
● Learning from data
● Can combine data and formal descriptions
● High transparency
AML vs Traditional approaches to Machine Learning

6. What is AML?
Symbolic AI:
● Uses symbols
● Symbols represent real world objects
● Mostly uses discrete mathematics
● Symbols are permanent
Statistical Learning (including Neural Networks):
● Uses parameters
● Parameters can map to the world or to intermediate
internal descriptions
● Mostly uses continuous mathematics
● Parameters can change
Algebraic Machine Learning:
● Uses symbols
● Symbols can map to the world (constants) or to
intermediate internal descriptions (atoms)
● Uses discrete mathematics
● Can create new symbols
● Symbols can change
AML vs Traditional approaches to Machine Learning

7. What is AML?
Main Algebraic Machine Learning features
Less sensitive to
statistical features
of training data
No tradeoff between
memorization and
learning (no overfitting)
High mathematical
transparency
Large-scale
distributed learning
Interactive ML

8. What is AML?
Main Algebraic Machine Learning features
Less sensitive to
statistical features
of training data
High mathematical
transparency
Distributed ML
ecosystem
Interactive ML
No tradeoff between
memorization and
learning (no overfitting)

9. Less sensitive to
statistical features
of training data
High mathematical
transparency
Distributed ML
ecosystem
Interactive ML
No tradeoff between
memorization and
learning (no overfitting)
What is AML?
Main Algebraic Machine Learning features

10. What is AML?
Main Algebraic Machine Learning features
Less sensitive to
statistical features
of training data
No tradeoff between
memorization and
learning (no overfitting)
High mathematical
transparency
Distributed ML
ecosystem
Interactive ML

11. What is AML?
Main Algebraic Machine Learning features
Less sensitive to
statistical features
of training data
No tradeoff between
memorization and
learning (no overfitting)
High mathematical
transparency
Large-scale
distributed learning
Interactive ML

12. What is AML?
Main Algebraic Machine Learning features
Less sensitive to
statistical features
of training data
No tradeoff between
memorization and
learning (no overfitting)
High mathematical
transparency
Large-scale
distributed learning
Interactive ML

13. AML - Proven Cases
Handwritten
digit recognition
- Supervised learning (MNIST)
- Atoms: algebraic elements
resulting from learning
Classification of human
motion
- Learning from both formal knowledge
and data (OPPORTUNITY)
- Activity recognition.
Queens
completion problem
- Learning from formal knowledge
- Rules encoded in the algebra
- AML understands the game from
the beginning of learning process
Proven cases of Algebraic Machine Learning

14. AML - Ongoing case studies
Ongoing Algebraic Machine Learning applications
World models
Robot control and
path planning
Interaction with
gesture keyboard
- Gesture keyboard
- Confidence feedback interface
- Robot control and landscape
navigation
- Formal description of high level
real-world concepts
- Ethical aspects

15. ALMA contribution to ROS 2
Develop an open-source decentralized AI integrating platform compatible with ROS 2
● AI-IP for cloud and edge computing environments
● Extend AI-IP to work with robotic and constrained device platforms.
ROS 2 as the primary robotic platform
● Open-source experimentation tools for ROS 2 community
eProsima is betting on AML as a key disrupting AI technology for robotics
● Integration of AML into a publish-subscribe framework compatible with ROS 2
1
2
Decentralized AI Integrating Platform fully compatible with ROS 2

16. Agenda
01 Human Centric Algebraic Machine Learning
ALMA
03 Serving models to ROS 2 robots, with Vulcanexus
environment
Enabling ML technologies for ROS 2
02 Application Aware, Life-Cycle Oriented Model-HW Co-Design
Framework for Sustainable, Energy Efficient ML Systems
SustainML

17. SustainML Mission
To provide a new SUSTAINABLE and INTERACTIVE ML
framework development for Green AI that will:
● Comprehensively prioritize and advocate energy
efficiency across the entire lifecycle of an application
● Avoid AI-waste
Spain
Germany
France
Denmark
Switzerland

18. SustainML framework
How the SustainML framework will work?
Developers describe the
tasks to solve a specific
problem
Describe the problem Framework analyze the tasks,
divide and encode the problem into
an abstract functional semantic
Encode the tasks
SustainML
framework
interactive
process
Developers can reconfigure the model with
other cores with optional pretrained
parameters or design their own models
Interactive design process
Framework suggests ML models with knowledge
transfer and recycling from its collection of Neural
Networks knowledge cores
Model suggestion
The framework will convey top tier AI experts’
experience by suggesting best practices, more
efficient alternatives or avoiding previous
negative results
Framework understands the problem

19. Not yet another AI studio tool!
SustainML project will focus on:
● Investigate the detailed footprint on computing and data HW
● Develop novel HW accelerators optimized for different layers and operations
The user will see the estimated CO2 footprint, HW resource and training
time during design process (before these costs actually occur!).
Useful for any user:
● Novice: what functionality different parts of a NN carries and why certain model
structures are better suited for specific tasks
● Intermediate/Experts: leverage SustainML framework to develop efficient models
optimized for their goals.
● AI researchers: skip the problem describing process and use the framework to
benchmark and optimize their ML models’ carbon footprint
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20. Agenda
02 Application Aware, Life-Cycle Oriented Model-HW Co-Design
Framework for Sustainable, Energy Efficient ML Systems
SustainML
01 Human Centric Algebraic Machine Learning
ALMA
03 Serving models to ROS 2 robots, with Vulcanexus
environment
Enabling ML technologies for ROS 2

21. Inference Server
21
LAN scenario

22. Inference Server
22
LAN scenario - Inference as a ROS 2 service

23. Inference service
23
Service Client and Server

24. Inference service
24
Service Client and Server

25. Inference service
25
Service Client and Server

26. Inference Server
26
LAN scenario

27. Inference Server
27
WAN scenario

28. eProsima DDS Router
28
Bridge DDS communications over WAN

29. eProsima DDS Router
29
Avoiding data introspection
achieves a zero-copy
system in data forwarding.
Efficient data routing
eProsima DDS Router features
Deploy intermediate nodes
to discover new entities
that enter and leave the
network dynamically.
Design for distributed
DDS networks
Supports WAN over TCP
communication to establish
DDS communications over
the Internet.
WAN communications
over TCP
Easily configurable modular
system for users with no
knowledge of computer
networks.
Easy deployment
Forward the user data
belonging to the specified
topics
Topic whitelisting

30. eProsima DDS Router
30
eProsima DDS Router for communication of edge devices with the Cloud.

31. Inference Server
31
WAN scenario

32. Inference Server
32
WAN scenario
Services support
currently under
development.

33. AI - Integrating Platform
Inference / Classification
Use The model to inference attributes over
the data received.
Train Model
Use the available data to find a better
model.
Publish Best Model
Publish the best model found via DDS topic.
Publish whole model or updates.
Install Best Model
Change the current model for the best
received.
Collect new labeled data to reinforce the
model.
AI
Integrating
Platform
Collect Data
Edge Side
Final Robot communicating via DDS.
Inference in edge.
Server Side
Great computation power.
Train new models.
33
Main features of AI - IP

34. Inference Server
34
WAN scenario (multiple servers)

35. AI - Integrating Platform
35
Distributed learning scenario

36. AI - Integrating Platform
36
Collaborative learning scenario

37. Vulcanexus
37
The All-in-One ROS 2 tool set
Easy to download and configure
Customize your experience with ROS 2
WAN communication out of the box
Fully synchronized with Fast-DDS updates

38. www.eProsima.com
Linkedin.com/company/eProsima Twitter.com/EProsima

39. Annex

40. Real-Time model update
Update to best model found
in the network in run-time
automatically
Parallel training
Multiple nodes could be
searching for the best
solution at the same time
Free Edge of training
Reduce edge device
computation effort to the
strictly needed
ML Model independent
Platform could be used
independently of the ML
model
Neural Networks, Random
Forest, AML, etc.
Collaborative network
Everyone gets benefited by
collaborating into finding the
best solution
Private dataset
Sharing the trained model
and not the dataset keeps
your data private and safe
40
Features and Capabilities
AI - Integrating Platform

41. Advantages of a ROS 2 AI Integration WG leaded by eProsima
Use the effort invested in ALMA and
its results to enhance the
integration of AI in ROS 2
Seamless use the AI Integrating
Platform in the ROS 2 ecosystem
Support for early use of AML in ROS
2
Discuss state-of-the-art topics with
experts on the field taking advantage
of ALMA partners
Promote contribution to
open-source AI integration
tools for ROS 2
Dissemination of ROS 2 by means of
ALMA conferences
ROS 2 AI Integration WG
41

42. Inference Server
42
WAN scenario (multiple servers)