Human learning and reasoning is a process that involves information obtained from a range of di erent senses combined to form an incredibly successful cognitive system. Arti cial autonomous systems should also e ectively process and combine di erent sensory information to compliment each other to produce better logical inferences. Through semantic modeling of low level features within a scenario, robots can generate representation of such features in level of abstraction where logical reasoning methods could be applied to them for decision making. Furthermore, at such level more than one modalities can be fused to compliment each other and produce logical inferences. This creates the possibility of robust decision making even under scenarios where certain modalities under perform, such as generic task performances. Lately heterogeneous cognitive systems have become quite popular among the research community, specially those using deep learning techniques over images and language sources, showing promising results. This workshop provides a uniquely focused forum for the discussion of the intersection of di erent elds like, audio, speech, language, images and some others into unique robotics systems that can auto-improve by learning and can be exploited through di erent reasoning techniques. This workshop will bring together the foremost researchers from di erent elds of robotics sharing and unifying techniques that can be applied to di erent areas on where they are currently used. Along with the presentation of novel works in the eld some discussions will be encouraged to share latest advances among researchers. Finally prominent gures on the research of multimodal semantic systems will be invited to share their latest and most successful achievements and overviews on the eld.
Multimodal knowledge representation in robotic systems
Multimodal semantic reasoning on robotic systems Shallow and deep semantic processing of heterogeneous information Computational aspects on robots for multimodal semantic Methodologies and approaches for multimodal semantic annotation Representing and resolving semantic multimodal ambiguity Hybrid symbolic and statistical approaches to representing multimodal semantics
Acknowledgements We want to specially thank our invited speakers (Mr. Serge Palaric, Senior Director EMEA Embedded for NVIDIA, Dr. Javier Civera from the Robotics from the Perception and Real-Time Group of the University of Zaragoza, Spain and Dr. Konstantin Schauwecker Founder of Nerian Vision Technologies) and to all members of the Advisory Committee (Luis Fernando D'Haro, Andreea I. Niculescu and Aravindkumar Vijayalingam) and the scienti c committee for their valuable contribution and suggestions. In addition, we thank the Human Language Technology department at Institute for Infocomm Research (I2R-A*STAR) and TUM Create, Singapore, for their support to this workshop.
October 20, 2015 Singapore
Rafael E. Banchs Suraj Nair