A cognitive control architecture for the perception-action cycle in robots and agents

Cutsuridis, Vassilis and Taylor, John G. (2013) A cognitive control architecture for the perception-action cycle in robots and agents. Cognitive Computation, 5 (3). pp. 383-395. ISSN 1866-9956

Documents
CutTay2013CognComput.pdf

Request a copy
[img] PDF
CutTay2013CognComput.pdf - Whole Document
Restricted to Repository staff only

904kB
Item Type:Article
Item Status:Live Archive

Abstract

We show aspects of brain processing on how
visual perception, recognition, attention, cognitive control,
value attribution, decision-making, affordances and action
can be melded together in a coherent manner in a cognitive
control architecture of the perception–action cycle for
visually guided reaching and grasping of objects by a robot
or an agent. The work is based on the notion that separate
visuomotor channels are activated in parallel by specific
visual inputs and are continuously modulated by attention
and reward, which control a robot’s/agent’s action repertoire.
The suggested visual apparatus allows the robot/
agent to recognize both the object’s shape and location,
extract affordances and formulate motor plans for reaching
and grasping. A focus-of-attention signal plays an instrumental
role in selecting the correct object in its corresponding
location as well as selects the most appropriate
arm reaching and hand grasping configuration from a list of
other configurations based on the success of previous
experiences. The cognitive control architecture consists of
a number of neurocomputational mechanisms heavily
supported by experimental brain evidence: spatial saliency,
object selectivity, invariance to object transformations,
focus of attention, resonance, motor priming, spatial-tojoint
direction transformation and volitional scaling of
movement.

Keywords:Saliency, Attention, Adaptive resonance theory, Decision-making, Value, Reaching, Grasping, Affordances, Perception–action cycle
Subjects:G Mathematical and Computer Sciences > G750 Cognitive Modelling
Divisions:College of Science > School of Computer Science
Related URLs:
ID Code:27727
Deposited On:26 Jun 2017 11:28

Repository Staff Only: item control page