Human-Robot Plan Communication
Abstract
Although the technical development of robots has made them more autonomous,
they still benefit from some human skills and advice. Therefore, the robot needs
to interact with a human and ask him for help to get out of difficulties in the
best way. This dissertation addresses the problem of finding better mechanisms
to communicate to a robot the directions for navigation in indoor environments.
We identify which out of a set of combinations of speech, gestures, and drawing
mechanisms are the most comfortable, easier to learn, and least error-prone for
human users. Three different methods: a Speaking method, a Gesturing and
Speaking method, and a Drawing method were tested for guiding the NAO robot
to the desired place, assuming that the robot has no prior map of the building
and even no information about the involved human faces. Our experiment consists
of having participants ask the robot to accomplish a complex indoor navigation
task. Task communication is attempted with each of the above methods. In
one experiment, no additional details were provided about the proposed methods
to help the participants, except for a video showing the problem in neuter human
terms. In subsequent tests, the participants were also given restrictions concerning
the robot communication capabilities: only to use speech, gestures, or vision. In
the third set of tests, the participants were also given training using examples of efficient communication with corresponding methods. The task was decomposed,
with points assigned for each component. The methods have been investigated and
evaluated based on the average task success in simulated plan execution. In our
results, the Drawing method leads to the highest level of task accomplishment, and
it is also the fastest communication method. Further, according to the final short
questionnaire, most participants feel that the Drawing method is more comfortable
and they can use it immediately without thinking, as compared to methods based
on speech.