Dataset from: In-situ bidirectional human-robot value alignment

A prerequisite for social coordination is bidirectional communication
between teammates, each playing two roles simultaneously: as receptive
listeners and expressive speakers. For robots working with humans in
complex situations with multiple goals that differ in importance, failure
to fulfill the expectation of either role could undermine group
performance due to misalignment of values between humans and robots.
Specifically, a robot needs to serve as an effective listener to infer
human users' intents from instructions and feedback, and as an
expressive speaker to explain its decision processes to users. In this
paper, we investigate how to foster effective bidirectional human-robot
communications in the context of value alignment---collaborative robots
and users form an aligned understanding of the importance of possible task
goals. We propose an explainable artificial intelligence (XAI) system in
which a group of robots predicts users' values by taking in-situ
feedback into consideration, while communicating their decision processes
to users through explanations. To learn from human feedback, our XAI
system integrates a cooperative communication model for inferring human
values associated with multiple desirable goals. To be interpretable to
humans, the system simulates human mental dynamics and predicts optimal
explanations using graphical models. We conducted psychological
experiments to examine the core components of the proposed computational
framework. Our results show that real-time human-robot mutual
understanding in complex cooperative tasks is achievable with a learning
model based on bidirectional communication. We believe this interaction
framework can shed light on bidirectional value alignment in communicative
XAI systems, and more broadly, in future human-machine teaming systems.