Abstract:

With the rapid development of robotics, robots have expanded their applications from industry and production lines to daily life. Beside servants, they can be pets, companions, or guides. In the near future, robots will appear in 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 Dynamic Spatial Behavior Cognition Model (Dynamic SBCM) of the robot. The model makes robots learn the specific, invisible rules in human society, and successively attune the learning result when robots are operated in the learned environment, or other similar environments.

Robots use inverse reinforcement learning (IRL) to learn the behavior by apprenticing human behavior. However, the perception for everyone feeling the same environment may not be identical, so the different perception will cause the different action. The thesis separates actions into many states using information entropy. Robots learn each state to represent the social rule more precisely. Bedsides, the robot also need to modify the learned result when operating to adapt to the dynamic environment.

The thesis includes a demonstration of a method of using the same learning approach to cluster trajectories by three velocity levels, slow, medium and fast, to describe the preference of human, and the corresponding cost function can predict the human preference.