Rachel Gockley
Robotics Institute
Carnegie Mellon University
Abstract:
This thesis addresses the problem of robots navigating in populated environments. Because traditional obstacle-avoidance algorithms do not differentiate between people and other objects in the environment, this thesis argues that such methods do not produce socially acceptable results. Rather, robots must detect people in the environment and obey the social conventions that people use when moving around each other, such as tending to the right side of a hallway and respecting the personal space of others. By moving in a human-like manner, a robot will cause its actions to be easily understood and appear predictable to people, which will facilitate its ability to interact with people and thus to complete its tasks.
We are interested in general spatial social tasks, such as navigating through a crowded hallway, as well as more cooperative tasks, such as accompanying a person side-by-side. We propose a novel framework for representing such tasks as a series of navigational constraints. In particular, we argue that each of the following must be considered at the navigational level: the task definition, societal conventions, and efficiency optimization. This thesis provides a theoretical basis for each of these categories. We propose to validate this conceptual framework by using it to design a simple navigational algorithm that will allow a robot to move through a populated environment while observing social conventions. We will then extend this algorithm within the framework to allow a robot to escort a person side-by-side. Finally, we will examine how human-like and appropriate the robot's behavior is in controlled user studies.
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