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This book constitutes the refereed proceedings of the 6th International Conference on Social Robotics, ICSR 2014, held in Sydney, NSW, Australia, in October 2014. The 41 revised full papers presented in this book were carefully reviewed and selected from numerous submissions. Amongst others, topics covered are such as interaction and collaboration among robots, humans, and environments; robots to assist the elderly and persons with disabilities; socially assistive robots to improve quality of life; affective and cognitive sciences for socially interactive robots; personal robots for the home; social acceptance and impact in the society; robot ethics in human society and legal implications; context awareness, expectation, and intention understanding; control architectures for social robotics; socially appealing design methodologies; safety in robots working in human spaces; human augmentation, rehabilitation, and medical robots; robot applications in education, entertainment, and gaming; knowledge representation and reasoning frameworks for robot social intelligence; cognitive architectures that support social intelligence for robots; robots in the workplace; human-robot interaction; creative and entertaining robots.

Robotic agents, such as autonomous office couriers or robot tourguides, must be both reliable and efficient. Thus, they have to flexibly interleave their tasks, exploit opportunities, quickly plan their course of action, and, if necessary, revise their intended activities. This book makes three major contributions to improving the capabilities of robotic agents: - first, a plan representation method is introduced which allows for specifying flexible and reliable behavior - second, probabilistic hybrid action models are presented as a realistic causal model for predicting the behavior generated by modern concurrent percept-driven robot plans - third, the system XFRMLEARN capable of learning structured symbolic navigation plans is described in detail.

In recent years, autonomous robots, including Xavier, Martha [1], Rhino [2,3], Minerva, and Remote Agent, have shown impressive performance in long-term demonstrations. In NASA’s Deep Space program, for example, an - tonomous spacecraft controller, called the Remote Agent [5], has autonomously performed a scienti? c experiment in space. At Carnegie Mellon University, Xavier [6], another autonomous mobile robot, navigated through an o? ce - vironment for more than a year, allowing people to issue navigation commands and monitor their execution via the Internet. In 1998, Minerva [7] acted for 13 days as a museum tourguide in the Smithsonian Museum, and led several thousand people through an exhibition. These autonomous robots have in common that they rely on plan-based c- trol in order to achieve better problem-solving competence. In the plan-based approach, robots generate control actions by maintaining and executing a plan that is e? ective and has a high expected utility with respect to the robots’ c- rent goals and beliefs. Plans are robot control programs that a robot can not only execute but also reason about and manipulate [4]. Thus, a plan-based c- troller is able to manage and adapt the robot’s intended course of action — the plan — while executing it and can thereby better achieve complex and changing tasks.

In this book, the author presents a new computational model of forestalling common flaws in autonomous robot behavior. To this end, robots are equipped with structured reactive plans (SRPs) which are concurrent control programs that can not only be interpreted but also be reasoned about and manipulated. The author develops a representation for SRPs in which declarative statements for goals, perceptions, and beliefs make the structure and purpose of SRPs explicit and thereby simplify and speed up reasoning about SRPs and their projections; furthermore a notation is introduced allowing for transforming and manipulating SRPs. Using this notation, a planning system can diagnose and forestall common flaws in robot plans that cannot be dealt with in other planning representations. Finally the language for writing SRPs is extended into a high-level language that can handle both planning and execution actions.