International Conference on Social Robotics

Special Sessions

There are two special sessions at ICSR 2014:

Papers submitted to the special sessions are full papers and appear in the conference proceedings. Papers are submitted to the special session using the same format, deadline and procedure as ordinary papers (during submission, there is a drop-down list to identify your work to the special session chairs). See the paper submission information for full details.

Papers affiliated to a special session will receive the same impartial review process applied to all non-affiliated papers. If the number of papers affiliated to the special session following the close of the paper submission period is insufficient, then the session may be dissolved and the papers assimilated into the main track of the conference.

Special Session on Knowledge Representation and Reasoning in Robotics

Robots and agents deployed to interact and collaborate with humans in homes, offices, and other domains, have to represent knowledge and reason at both the sensorimotor level and the cognitive (or social) level. This objective maps to the fundamental challenge of representing, revising, and reasoning with qualitative and quantitative descriptions of uncertainty and incomplete domain knowledge obtained from sensors, humans, and other sources. Although researchers in AI and robotics have developed many algorithms and architectures for knowledge representation and reasoning, the research community is fragmented, with separate vocabularies that are increasingly making it difficult for these researchers to communicate with each other. For instance, the rich body of research in logical reasoning paradigms and qualitative representations provides appealing commonsense reasoning capabilities, encodes semantics such that they are accessible to humans, and provides smaller state spaces for learning and reasoning. However such representations may not support probabilistic analysis, whereas a lot of information available to robots at the sensorimotor level is represented probabilistically to quantitatively model the uncertainty in sensor input processing and actuation. On the other hand, the sophisticated algorithms based on probabilistic graphical models that support quantitative modeling of this uncertainty, make it difficult to represent and reason with commonsense knowledge. Furthermore, algorithms developed to combine logical and probabilistic reasoning do not provide the desired expressiveness for commonsense reasoning and/or do not fully support the uncertainty modeling capabilities required in robotics.

This special session seeks to bring together researchers from these disparate communities, fostering an open discussion to promote a deeper understanding and appreciation of recent breakthroughs and tough challenges in the individual communities. We will build on the workshops and symposia that have been organized on this theme, and on the broader theme of bringing AI and robotics closer together, in the last few years. We hope that this special session will encourage collaborative efforts towards addressing the knowledge representation and reasoning challenges faced by robots interacting and collaborating with humans.


Mohan Sridharan, Texas Tech University
Subramanian Ramamoorthy, The University of Edinburgh
Vaishak Belle, University of Toronto

Special Session on Social Robots for Therapeutic Purposes

Robots can be used to help patients with various healthcare needs. These kinds of robots are already developed, such as nursing care robots, health condition managing robots, and companion-type robots, as well as used in various places in our society, such as hospitals, retirement villages, and home environments includes supporting independent living.

This Special Session will focus on the current advances in the area of social robots for therapeutic purposes, including techniques and designs of therapeutic robot and their clinical evaluation. Papers are solicited on all areas directly related to these topics, including but not limited to:


Ho Seok AHN, University of Auckland
Bruce A. MacDonald, University of Auckland