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Content archived on 2024-05-29

Perception, action and cognition through learning of object-action complexes

Objective

The successful design of a cognitive system must rely on a theoretical and measurable basis which on the one hand applies to humans and on the other hand to an artificial system, ultimately allowing for its construction. PACO-PLUS aims at the design of a cognitive robot that is able to develop perceptual, behavioural and cognitive categories in a measurable way and communicate and share these with humans and other artificial agents. PACO-PLUS brings together a consortium of robotics researchers, engineers, computer vision scientists, linguists, theoretical neuroscientists and cognitive psychologists, which is reflected in the management organization.

Central to the approach are three almost axiomatic assumptions which are linked to each other and which are the building blocks of a new approach required to create cognitive artificial agents:
- Objects and Actions are inseparably intertwined; the so-called Object-Action Complexes are the building blocks of cognition.
- Cognition is based on recurrent processes involving nested feedback loops operating on, contextualizing and reinterpreting object-action complexes. This is done while actively closing the perception-action cycle.
- Unified measure of success and progress exist through minimization of contingencies which an artificial cognitive system experiences while interacting with the environment or other agents.

To demonstrate the feasibility of our approach we will build robot systems with advanced cognitive capabilities that operate in real-world scenarios and that are able to learn to interact and perform basic communication with humans: A robot system to augment human action, and a robot system that is able to explore and manipulate a limited set of objects in an unconstrained environment.

Call for proposal

Data not available

Coordinator

UNIVERSITAET KARLSRUHE (TH)
EU contribution
No data
Address
KAISERSTRASSE 12
76131 KARLSRUHE
Germany

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Total cost
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Participants (9)