Robots and autonomous systems are advancing rapidly beyond traditional functions. This can lead to a mismatch with human expectations of system behaviors during interaction, especially when the systems behave unexpectedly. Unexpected system behaviors could induce negative emotional responses in humans, which not all systems have the capability of recognizing and detecting in real-time. To prevent such situations, systems should communicate expectations of system behavior to humans during the task. In addition, after a mismatch, the systems should perform post-hoc strategies to mitigate humans' negative emotional responses.
This thesis first investigates how systems can communicate expectations to humans in situ using legible motion planning based on Potential Field and Vector Field. Such an obstacle-aware intent-expressive motion planner could produce paths that are comparable to conventional methods.
Next, this thesis explores how systems can detect subtle emotional responses to unexpected system behaviors by designing and collecting data from a human study of participants interacting with a driving simulation system to perform non-critical tasks. Findings showed that participants' emotional responses to different stimuli, such as surprise, confusion, and frustration, could be distinguished based on facial action units, providing important insights for building a real-time autonomous emotional response detector.
Finally, this thesis studies post-hoc expectation mismatch mitigation strategies. Specifically, this thesis focuses on acknowledgment, apology, and explanation to mitigate negative emotional responses by humans using verbal, visual, and nonverbal modalities. Analyses showed that acknowledgment and apology as the sole mitigation strategy are not sufficient to influence people's emotional responses in driving simulator setting, and further actions from the system are preferred. Moreover, different modalities of explanation, such as verbal and visual, are comparably effective in reducing unexpectedness in robot behaviors, and such modes of explanation could retain their effectiveness cross-platform.