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Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.09341.2
URN: urn:nbn:de:0030-drops-23647
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2010/2364/
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Beetz, Michael ;
Brock, Oliver ;
Cheng, Gordon ;
Peters, Jan
09341 Summary -- Cognition, Control and Learning for Robot Manipulation in Human Environments
Abstract
High performance robot arms are faster, more accurate, and stronger
than humans.
Yet many manipulation tasks that are easily performed by humans as
part of their daily life are well beyond the capabilities of such
robots. The main reason for this superiority is that humans can rely
upon neural information processing and control mechanisms which are
tailored for performing complex motor skills, adapting to uncertain
environments and to not imposing a danger to surrounding humans. As
we are working towards autonomous service robots operating and
performing manipulation in the presence of humans and in human living
and working environments, the robots must exhibit similar levels of
flexibility, compliance, and adaptivity.
The goal of this Dagstuhl seminar is to make a big step towards
pushing robot manipulation forward such that robot assisted living can
become a concrete vision for the future.
In order to achieve this goal, the computational aspects of everyday
manipulation tasks need to be well-understood, and requires the
thorough study of the interaction of
perceptual, learning, reasoning, planning, and control mechanisms.
The challenges to be met include cooperation with humans, uncertainty
in both task and environments, real-time action requirements, and the
use of tools. The challenges cannot be met by merely improving the
software engineering and programming techniques. Rather the systems
need built-in capabilities to deal with these challenges. Looking at
natural intelligent systems, the most promising approach for handling
them is to equip the systems with more powerful cognitive mechanisms.
The potential impact of bringing cognition, control and learning
methods together for robotic manipulation can be enormous. This urge
for such concerted approaches is reflected by a large number of
national and international research initiatives including the DARPA
cognitive systems initiative of the Information Processing Technoloy
Office, various integrated projects funded by the European Community,
the British Foresight program for cognitive systems, huge Japanese
research efforts, to name only a few.
As a result, many researchers all over the world engage in cognitive
system research and there is need for and value in discussion. These
discussions become particularly promising because of the growing
readiness of researchers of different disciplines to talk to each
other.
Early results of such interdisciplinary crossfertilization can already
be observed and we only intend to give a few examples: Cognitive
psychologists have presented empirical evidence for the use of
Bayesian estimation and discovered the cost functions possibly
underlying human motor control. Neuroscientists have shown that
reinforcement learning algorithms can be used to explain the role of
Dopamine in the human basal ganglia as well as the functioning of the
bea brain. Computer scientists and engineers have shown that the
understanding of brain mechanisms can result into realiable learning
algorithms as well as control setups. Insights from artificial
intelligence such as Bayesian networks and the associated reasoning
and learning mechanisms have inspired research in cognitive
psychology, in particular the formation of causal theory in young
children.
These examples suggest that (1)~successful computational mechanisms in
artificial cognitive systems tend to have counterparts with similar
functionality in natural cognitive systems; and (2)~new consolidated
findings about the structure and functional organization of perception
and motion control in natural cognitive systems indicate in a number
of cases much better ways of organizing and specifying computational
tasks in artificial cognitive systems.
BibTeX - Entry
@InProceedings{beetz_et_al:DagSemProc.09341.2,
author = {Beetz, Michael and Brock, Oliver and Cheng, Gordon and Peters, Jan},
title = {{09341 Summary – Cognition, Control and Learning for Robot Manipulation in Human Environments}},
booktitle = {Cognition, Control and Learning for Robot Manipulation in Human Environments},
pages = {1--5},
series = {Dagstuhl Seminar Proceedings (DagSemProc)},
ISSN = {1862-4405},
year = {2010},
volume = {9341},
editor = {Michael Beetz and Oliver Brock and Gordon Cheng and Jan Peters},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2010/2364},
URN = {urn:nbn:de:0030-drops-23647},
doi = {10.4230/DagSemProc.09341.2},
annote = {Keywords: Mobile manipulation, cognition, control, learning, humanoid robot, unstructured environments}
}
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Keywords: |
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Mobile manipulation, cognition, control, learning, humanoid robot, unstructured environments |
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Collection: |
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09341 - Cognition, Control and Learning for Robot Manipulation in Human Environments |
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Issue Date: |
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2010 |
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Date of publication: |
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07.01.2010 |
