Paolo Arena -
University of Catania
From Emergence to Cooperation: the role of Nonlinear Dynamics
in Cognitive Robotics
ABSTRACT:
Living creatures show distinct abilities to interact adaptively with their environment.
These characteristics find their roots in the self-organizing dynamics of neural circuits,
which in nonlinear science represent the highest example of emergent behavior. The lecture will
explore the paradigm of biological inspiration for modelling and implementation of adaptive
locomotion patterns in biological inspired walking machines.
Once assessed the potentiality of lattices of nonlinear artificial neurons to lead to the
emergence adaptive locomotion controllers, endowed with graceful degradation purposes, the
fascinating world of perception will be faced with. In fact, it is natural, once developing
legged robots able to suitably move, to try to face with the problem of autonomous action
planning and environment intelligent interaction.
Even if the term perception is being used more and more frequently in this period, rarely it
is referred to considering nonlinear dynamical circuits and systems. In fact it is mostly
related to psychological theories, neurophysiological experiments or computer programs.
However, it is clear that perceptive information in living systems uses, as substrate,
massively connected cells, mutually and massively interacting. In our framework the core of
perception is conceived as an emergent, pattern forming, phenomenon.
In our spatial-temporal approach, perception is considered as the result of a dynamic pattern
forming process, in which a particular pattern will evolve in a spatial-temporal structure,
starting from the information deriving from sensors. This pattern will indeed represent in a
concise fashion the environment information. Recent results in neurobiology have shown that
this is based on internal representations that combine aspects of sensory input and motor
output in an unified way.
This is the essence from which percepts are able to be produced in real time for guiding actions
in complex environment.
Roberto Battiti - University of Trento
Reactive Search: Adaptive on-line Self-Tuning for Optimization
ABSTRACT:
Most state-of-the-art heuristics are characterized by a certain number of
choices and free parameters, whose appropriate setting is a subject that raises
issues of research methodology. In some cases the role of the user as an intelligent
(learning) part makes the reproducibility of heuristic results difficult and, as a
consequence, the competitiveness of alternative techniques depends in a crucial way
on the user's capabilities. Reactive Search advocates the use of simple sub-symbolic
machine learning to automate the parameter tuning process and make it an integral
(and fully documented) part of the algorithm. The word "reactive" hints at a ready
response to events during the search through an internal online feedback loop for the
self-tuning of critical parameters. Task-dependent and local properties of the configuration
space can be used by the algorithm to determine the appropriate balance between
diversification and intensification. Some interesting novel research directions
combining cooperative strategies and Reactive Search will be highlighted.
Marco Dorigo -
Université Libre de Bruxelles
Swarm-bots: An Experiment in Swarm Robotics
ABSTRACT:
Swarm robotics is the study of how collectively intelligent behaviors can emerge
from local interactions of a large number of relatively simple physically embodied agents.
In this talk I will discuss results of the Swarm-bots experiment in swarm robotics. A swarm-bot
is an artifact composed of a swarm of assembled s-bots. The s-bots are mobile robots capable
of connecting to, and disconnecting from, other s-bots. In the swarm-bot form, the s-bots are
attached to each other and, when needed, become a single robotic system that can move and
change its shape. S-bots have relatively simple sensors and motors and limited computational
capabilities. A swarm-bot can solve problems that cannot be solved by s-bots alone. In the talk,
I will shortly describe the s-bots hardware and the methodology we followed to develop algorithms
for their control. Then I will focus on the capabilities of the swarm-bot robotic system by
showing video recordings of some of the many experiments we performed to study coordinated movement,
path formation, self-assembly, collective transport, shape formation, and other collective behaviors.