ALMA, the project aiming to leverage Algebraic Machine Learning properties, brings in this presentation a plan to make the ALMA decentralized AI integrating platform compatible with ROS 2.

1. This project has received funding from the European Union's Horizon 2020
research and innovation programme under grant agreement No 952091.
February 28, 2022
HUMAN CENTRIC ALGEBRAIC MACHINE LEARNING
-Integration of AI in ROS 2 ecosystem-
ALMA

2. H2020-EIC-FETPROACT-2019
December 16, 2021 2
ALMA Consortium
Proyectos y Sistemas de
Mantenimiento SL (eProsima)
German Research Center for
Artificial Intelligence
Technical Research Centre of Finland
FIWARE Foundation e.V.

3. H2020-EIC-FETPROACT-2019
December 16, 2021 3
eProsima contribution
eProsima contribution to ALMA
1 ALMA project manager
2 Architecture and technical coordination
3 Leader of dissemination, exploitation and collaboration
4 System and tools development: AI Integrating Platform (AI-IP)

4. H2020-EIC-FETPROACT-2019
December 16, 2021
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
ALMA contribution to ROS 2
4
Decentralized AI integrating platform compatible with ROS 2
1
2

5. H2020-EIC-FETPROACT-2019
December 16, 2021
ALMA contribution to ROS 2
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Collaboration and exploitation of ROS 2 AI integration
As leader of the dissemination, exploitation and collaboration work package
● Target the ROS 2 community and participate in its events (ROSCON)
● Organize open workshops with other project partners and experts in the fields on:
○ AI in robotics and constrained devices
○ New mathematical frameworks in AI
○ Application of ethics and cultural standards to AI
○ Human-Computer Interaction
Collaborate with ROS 2 to build open-source versions of ALMA results
● Main project results released as OSS and integrated in ROS 2 using the well established
communication channels of eProsima with this organization (ROS 2 TSC).
3
4

6. H2020-EIC-FETPROACT-2019
December 16, 2021
AI integration in robotic environments
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Use case example - AI integration in ironing robot TEO

7. H2020-EIC-FETPROACT-2019
December 16, 2021
Algebraic Machine Learning (AML)
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Research in a new Machine Learning paradigm based on Algebra
ALGEBRAIC MACHINE LEARNING
Machine Learning from semantic embeddings of data and
formal knowledge into discrete algebraic structures

8. H2020-EIC-FETPROACT-2019
December 16, 2021
Algebraic Machine Learning (AML)
8
Research in a new Machine Learning paradigm based on Algebra
Less sensitive to
statistical features
of training data
Combine
unstructured data
with formal
specifications of
human knowledge
High mathematical
transparency
Distributed ML
ecosystem
Interactive
human-centric ML

9. H2020-EIC-FETPROACT-2019
December 16, 2021
Algebraic Machine Learning (AML)
9
Research in a new Machine Learning paradigm based on Algebra
Less sensitive to
statistical features
of training data
Combine
unstructured data
with formal
specifications of
human knowledge
High mathematical
transparency
Distributed ML
ecosystem
Interactive
human-centric ML

10. H2020-EIC-FETPROACT-2019
December 16, 2021
Algebraic Machine Learning (AML)
10
Research in a new Machine Learning paradigm based on Algebra
Less sensitive to
statistical features
of training data
Combine
unstructured data
with formal
specifications of
human knowledge
High mathematical
transparency
Distributed ML
ecosystem
Interactive
human-centric ML

11. H2020-EIC-FETPROACT-2019
December 16, 2021
Algebraic Machine Learning (AML)
11
Research in a new Machine Learning paradigm based on Algebra
Less sensitive to
statistical features
of training data
Combine
unstructured data
with formal
specifications of
human knowledge
High mathematical
transparency
Distributed ML
ecosystem
Interactive
human-centric ML

12. H2020-EIC-FETPROACT-2019
December 16, 2021
Algebraic Machine Learning (AML)
12
Research in a new Machine Learning paradigm based on Algebra
Less sensitive to
statistical features
of training data
Combine
unstructured data
with formal
specifications of
human knowledge
High mathematical
transparency
Distributed ML
ecosystem
Interactive
human-centric ML

13. H2020-EIC-FETPROACT-2019
December 16, 2021
Algebraic Machine Learning (AML)
13
Research in a new Machine Learning paradigm based on Algebra
Less sensitive to
statistical features
of training data
Combine
unstructured data
with formal
specifications of
human knowledge
High mathematical
transparency
Distributed ML
ecosystem
Interactive
human-centric ML

14. H2020-EIC-FETPROACT-2019
December 16, 2021
Human-Machine Interaction
14
Methods for ensuring effective human interaction with intelligent systems

15. H2020-EIC-FETPROACT-2019
December 16, 2021
Learning world models, ethics and culture
15
Standards of ethics and human culture in AI applications

16. H2020-EIC-FETPROACT-2019
December 16, 2021
Hardware acceleration for ML
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Develop a high performance AML engine for heterogeneous compute platforms

17. H2020-EIC-FETPROACT-2019
December 16, 2021
AI Integrating Platform
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Distributed platform for integrating AI into ROS 2 ecosystems

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December 16, 2021
AI Integrating Platform
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Distributed platform for integrating AI into ROS 2 ecosystems

19. H2020-EIC-FETPROACT-2019
December 16, 2021
AML collaborative learning
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Basic architecture of collaborative learning of AML models

20. H2020-EIC-FETPROACT-2019
December 16, 2021
ROS 2 AI Integration WG
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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

21. Thanks for listening
We'd be pleased to answer any question you may have
H2020-EIC-FETPROACT-2019
December 16, 2021
alma@eprosima.com
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ALMA contact:
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https://alma-ai.eu

22. H2020-EIC-FETPROACT-2019
December 16, 2021
Human-Machine Interaction
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Methods for ensuring effective human interaction with intelligent systems
1 2 3 4
Interaction Paradigm
Methodology
Design and Develop a
Working Prototype
Develop and apply
evaluation methods
Cognitive foundations
for Human AML
Interaction

23. H2020-EIC-FETPROACT-2019
December 16, 2021
Human-Machine Interaction
23
Methods for ensuring effective human interaction with intelligent systems
1 2 3 4
Interaction Paradigm
Methodology
Design and Develop a
Working Prototype
Develop and apply
evaluation methods
● More informative visualisations of intelligent algorithm
● Better communication to human users
● Understand how human learn from and ultimately control interaction with the AML
Cognitive foundations
for Human AML
Interaction

24. H2020-EIC-FETPROACT-2019
December 16, 2021
Human-Machine Interaction
24
Methods for ensuring effective human interaction with intelligent systems
1 2 3 4
Design and Develop a
Working Prototype
Develop and apply
evaluation methods
● Empower, rather than deskill human users over time
● Enable AI researchers, not just HCI researchers, to easily create more
effective human-computer partnerships, beyond any specific AI technique
Cognitive foundations
for Human AML
Interaction
Interaction Paradigm
Methodology

25. H2020-EIC-FETPROACT-2019
December 16, 2021
Human-Machine Interaction
25
Methods for ensuring effective human interaction with intelligent systems
1 2 3 4
Develop and apply
evaluation methods
● Demonstrate the design methodology
and interaction paradigm for an
effective human-computer partnership
Cognitive foundations
for Human AML
Interaction
Interaction Paradigm
Methodology
Design and Develop a
Working Prototype

26. H2020-EIC-FETPROACT-2019
December 16, 2021
Human-Machine Interaction
26
Methods for ensuring effective human interaction with intelligent systems
1 2 3 4
● Determine the efficacy of the interaction
from a human perspective
Cognitive foundations
for Human AML
Interaction
Interaction Paradigm
Methodology
Design and Develop a
Working Prototype
Develop and apply
evaluation methods

27. H2020-EIC-FETPROACT-2019
December 16, 2021
Learning world models, ethics and culture
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Limitations of ethics and human culture in AI applications
Current AI limitations
⚙ Human ability to correctly perceive, interpret and react to complex situations based on human
experience
⚙ Many AI applications are unable to acquire and use such background knowledge

28. H2020-EIC-FETPROACT-2019
December 16, 2021
⚙ Create AML based world models and context recognition systems
⚙ Human-AML co-creation of complex human concepts
⚙ Represent basic ethical ideas in AML
⚙ Define world models in terms of ecosystems’ cultures and ethics
⚙ Update AML-based world models with ethical concepts
⚙ Human-AML co-creation of cultural pluralism and cultural hybridization
Learning world models, ethics and culture
28
Standards of ethics and human culture applied to Artificial Intelligence