[ecoop-info] PhD position: Self-Adaptation and Resilience for Artificial Intelligence-Based Robotic Systems

[Ansgar Radermacher]

The LSEA (Embedded and Autonomous Systems Design Laboratory) at CEA LIST 
<http://www-list.cea.fr/en/talents-list/did-you-know> offers a PhD 
position:

Robotic systems have to integrate more and more functionality including 
autonomous decisions how to adapt to changing environment conditions or 
failures. These systems have to respect classical requirements of 
embedded systems (resource, timeliness), but resilience to failures and 
safety requirements become very important. Stopping the system is not an 
option for instance for an autonomous vehicle or a drone, systems have 
to be fail-operational. Another aspect is the use of AI components 
(machine learning) in control algorithms and for taking autonomous 
decisions - these systems are attractive, as they are able to abstract 
and generalize by inductive inference. Whole they can offer excellent 
performance in nominal conditions, the validation of such 
decision-making system is complex. Even simple algorithms can create 
emergent behaviour when systems interact and when feedback loops are 
introduced. This raises the question how to validate the system 
behaviour and its ability to anticipate, resist, reconfigure itself 
after a fault or interference.

Autonomous systems must adapt to changing environmental conditions. 
Although the new configurations can be checked offline, this approach is 
not flexible enough, it only applies to the configurations planned at 
development time and does not scale because the number of potential 
configurations increases exponentially with the number of components (as 
examined in the European project SafeAdapt (www.safeadapt.eu). Thus, the 
system has to check at runtime whether new configurations meet the 
functional and non-functional constraints. The use of a model @ runtime 
is a way of attacking this problem.

The objective of this thesis is to embed validation and reconfiguration 
mechanisms into the running system while specifically taking AI 
applications into account that learn and evolve. A promising approach is 
resilient engineering which uses models as run-time central elements 
providing the opportunity to learn from past events, plan, and recover 
from a dangerous decision.

As the subject is very broad, we propose to work from the bottom up with 
case studies that seem representative in the field of collaborative 
robotics and autonomous vehicles (particularly drones).

A more detailed version of this text is available at linked-in 
<https://www.linkedin.com/pulse/phd-position-self-adaptation-resilience-artificial-ansgar-radermacher/>.

Applications shall be sent by e-mail to ansgar.radermacher at cea.fr

Applications have to contain detailed experience and background 
information and should contain a recommendation letter.

Best regards

Ansgar Radermacher

--
Ansgar Radermacher                CEA/DRT/DILS/LSEA
http://www-list.cea.fr/en/
phone: +33 16908 3812
mailto: ansgar.radermacher at cea.fr