Building Trust in a Human-Robot Team with Automatically Generated Explanations

December 1, 2015 | Orlando, FL

Speaker: Ning Wang

Technological advances offer the promise of robotic systems that work with people to form human-robot teams that are more capable than their individual members. Unfortunately, the increasing capability of such autonomous systems has often failed to increase the capability of the human-robot team. Studies have identified many causes underlying these failures, but one critical aspect of a successful human-machine interaction is trust. When robots are more suited than humans for a certain task, we want the humans to trust the robots to perform that task. When the robots are less suited, we want the humans to appropriately gauge the robots’ ability and have people perform the task manually. Failure to do so results in disuse of robots in the former case and misuse in the latter. Real-world case studies and laboratory experiments show that failures in both cases are common. Researchers have theorized that people will more accurately trust an autonomous system, such as a robot, if they have a more accurate understanding of its decisionmaking process. Studies show that explanations offered by an automated system can help maintain trust with the humans in case the system makes an error, indicating that the robot’s communication transparency can be an important factor in earning an appropriate level of trust. To study how robots can communicate their decision making process to humans, we have designed an agent-based online test-bed that supports virtual simulation of domain-independent human-robot interaction. In the simulation, humans work together with virtual robots as a team. The test-bed allows researchers to conduct online human-subject studies and gain better understanding of how robot communication can improve human-robot team performance by fostering better trust relationships between humans and their robot teammates. In this paper, we describe the details of our design, and illustrate its operation with an example human-robot team reconnaissance task.