HRI and Communication Dynamics

In considering how people perceive robots, I came up with a short list of archetypes that portray robots as Tools, Predators, Laborers, Caretakers, and Peers. This list overlaps with interaction roles defined by HRI theorists [2,4]:

• Operator—directly influences or controls another’s behavior
• Mechanic—adjusting technical parts of a whole
• Supervisor—overseeing the coordination of subordinate workers
• Mentor—teaching or leading others
• Teammate (Peer)—cooperating with others
• Bystander (Peer)—witnessing behavior and experiencing indirect effects
• Information Consumer—benefits from information being broadcast without direct control of behavior or mechanics

Although all of these roles were meant to describe a human interacting with a robot, I have intentionally generalized them to include the reciprocal notion of a robot interacting with a human. It is also important to note that these roles do not necessarily define the robot’s purpose. There may be situations where the interaction shifts from operator to information consumer between the same human and robot.

The distinction between these two lists highlights the role of perspective in setting expectations for robot performance. People come into a relationship with a robot with some preconceived notion of its purpose, and that may or may not match what the designer had in mind. A human’s orientation toward a robot impacts how they interaction [1]. This should be true across the functional types of robots—Search and rescue, Assistive and educational robots, Entertainment, Military and Police, Space exploration, and Unmanned Air/Underwater Vehicles—although both human and robot are oriented toward each other in different ways.

In the field, roboticists rely on multi-disciplinary methods to figure this out. On the technical side, a mix of physical and simulated interaction can help inform development. Simulated users can provide insights [3], but robotics relies on the creation of real systems perhaps moreso than other computer interaction. There are some standard metrics used for evaluation [5], but they may not apply to many of the emergent dynamics that will arise out of long-term use. Figuring out how to test human-robot interaction and what to look for is still an open question.

Goodrich and Shultz suggest that from the problems motivating HRI research (such as: requirements on autonomy, information sharing, and evaluation) emerge several solution themes:

Interaction Complexity. Most interesting HRI applications deal with exchanges in dynamic environments. Interactions and the behaviors that result must be able to function within the complexity of this environment as well as adapt to changing dynamics.

Telepresence and Information Fusion. Remote control operation of robots is the oldest form of HRI, but humans continue to be disconnected from the sensor processing and communication conducted through the robot. With more attention paid to the interfaces used and information exchanged during remote control sessions, robots may become more of an extension of the human and therefore enjoy more success in operation.

Cognitive Modeling. When humans interact with each other, effective communication and collaboration is build from shared experiences and cultures. This common ground creates realistic expectations. It is the same for a robot. By developing models of human reasoning and behavior, roboticists hope their creations are capable of recognizing and adjusting to human communication.

Team Organizations and Dynamics. More and more, the 1:1 human-robot interaction is evolving into many:many relationships. Multiple robots and managed by multiple humans. These groups are subject to the same human dynamics studied by communication and sociology researchers. Roboticists can help shape these dynamics by establishing organizational structures, protocols, and support tools.

Interactive Learning. With complex interactions, it is impossible to anticipate all problems with scripted responses Robots and humans collaborate to incrementally improve perception, autonomy, and interaction.

These five solution themes all deal with the evolution of human-robot relationships from perception to understanding.

References:

1. Dautenhahn, K., Woods, S., Kaouri, C., Walters, M., Koay, K. L., and Werry, I. (2005). What is a robot companion—friend, assistant, or butler? In: IEEE/Robotics Society of Japan International Conference on Intelligent Robots and Systems.
2. Goodrich, M.A., and Schultz, A.C. (2007). Human-robot interaction: A Survey. Foundations and Trends in Human-Computer Interaction, 1(3), pp 203-275.
3. Ritter, F. E., Van-Rooy, D., and St. Amant, R. (2005). Using a simulated user to explore human robot interfaces. In: ACT-R Workshop.
4. Scholtz, J., Theofanos, M., and Antonishek, B. (2002). Theory and evaluation of human robot interactions. In: 36th International Conference on Systems Sciences, Hawaii: IEEE.
5. Steinfeld, A., Fong, T., Kaber, D., Lewis, M., Scholtz, J., Schultz, A., and Goodrich, M. (2006). Common metrics for human-robot interaction. In: First ACM International Conference on Human Robot Interaction, Salt Lake City, UT, USA: ACM.