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. &nbsp

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. &nbsp

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