39 research outputs found
09341 Abstracts Collection -- Cognition, Control and Learning for Robot Manipulation in Human Environments
From 16.08. to 21.08.2009, the Dagstuhl Seminar 09341 ``Cognition, Control and Learning for Robot Manipulation in Human Environments \u27\u27 was held
in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor
Segmentation Benchmark (LITS) organized in conjunction with the IEEE
International Symposium on Biomedical Imaging (ISBI) 2016 and International
Conference On Medical Image Computing Computer Assisted Intervention (MICCAI)
2017. Twenty four valid state-of-the-art liver and liver tumor segmentation
algorithms were applied to a set of 131 computed tomography (CT) volumes with
different types of tumor contrast levels (hyper-/hypo-intense), abnormalities
in tissues (metastasectomie) size and varying amount of lesions. The submitted
algorithms have been tested on 70 undisclosed volumes. The dataset is created
in collaboration with seven hospitals and research institutions and manually
reviewed by independent three radiologists. We found that not a single
algorithm performed best for liver and tumors. The best liver segmentation
algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation
the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image
data and manual annotations continue to be publicly available through an online
evaluation system as an ongoing benchmarking resource.Comment: conferenc
Commissioning and performance of the CMS silicon strip tracker with cosmic ray muons
This is the Pre-print version of the Article. The official published version of the Paper can be accessed from the link below - Copyright @ 2010 IOPDuring autumn 2008, the Silicon Strip Tracker was operated with the full CMS experiment in a comprehensive test, in the presence of the 3.8 T magnetic field produced by the CMS superconducting solenoid. Cosmic ray muons were detected in the muon chambers and used to trigger the readout of all CMS sub-detectors. About 15 million events with a muon in the tracker were collected. The efficiency of hit and track reconstruction were measured to be higher than 99% and consistent with expectations from Monte Carlo simulation. This article details the commissioning and performance of the Silicon Strip Tracker with cosmic ray muons.This work is supported by FMSR (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ,
and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS
(Colombia); MSES (Croatia); RPF (Cyprus); Academy of Sciences and NICPB (Estonia);
Academy of Finland, ME, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG,
and HGF (Germany); GSRT (Greece); OTKA and NKTH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); NRF (Korea); LAS (Lithuania); CINVESTAV, CONACYT,
SEP, and UASLP-FAI (Mexico); PAEC (Pakistan); SCSR (Poland); FCT (Portugal); JINR (Armenia, Belarus, Georgia, Ukraine, Uzbekistan); MST and MAE (Russia); MSTDS (Serbia); MICINN and CPAN (Spain); Swiss Funding Agencies (Switzerland); NSC (Taipei); TUBITAK and TAEK (Turkey); STFC (United Kingdom); DOE and NSF (USA)
Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering
This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways.
 
Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering
This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways.
 
Context-Aware Image Acquisition Approaches for Renovation Building Process Using AR and Linked Data
Context-Aware Image Acquisition Approaches for Renovation Building Process Using AR and Linked Data
09341 Summary -- Cognition, Control and Learning for Robot Manipulation in Human Environments
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