Abstract:

With the rapid development of robotics, robots have expanded their presence beyond industrial environments and production lines, and have entered daily life. Beside servants, they can be pets, companions, or guides. In the near future, robots will appear in more and more human environments, such as campuses, offices, hospitals, museums and even households. For robots to be useful, and to be accepted by humans, they need to understand human behaviors as well as to adapt to, and relate with, their environments. Human behaviors, however, are highly affected by implicit human factors such as culture, social conventions, laws and even the mental states of individuals and groups. If robots are to be accepted by humans, they must conform to common social norms and local customs as well as recognize highly socialized spatial behaviors.

The main concept of this thesis is to develop the Generalized Spatial Behavior Cognition Model (GSBCM). This model teaches robots how to learn special, implicit rules for various environments. In addition to describing the theory in detail, the thesis provides examples of several scenarios of human environments in which the theory can be applied to practical use. We show that this approach enables robots to accumulate the knowledge needed to ensure good behavior in almost any social situation.

The thesis includes a demonstration of a method using the same framework to reason human preferences. Armed with this knowledge, the robot can respond to or interact with humans appropriately, and thus being less likely to cause offence and more likely to be acceptable in society.