  With the advent of AI technologies, humans and robots are increasingly
teaming up to perform collaborative tasks. To enable smooth and effective
collaboration, the topic of value alignment (operationalized herein as the
degree of dynamic goal alignment within a task) between the robot and the human
is gaining increasing research attention. Prior literature on value alignment
makes an inherent assumption that aligning the values of the robot with that of
the human benefits the team. This assumption, however, has not been empirically
verified. Moreover, prior literature does not account for human's trust in the
robot when analyzing human-robot value alignment. Thus, a research gap needs to
be bridged by answering two questions: How does alignment of values affect
trust? Is it always beneficial to align the robot's values with that of the
human? We present a simulation study and a human-subject study to answer these
questions. Results from the simulation study show that alignment of values is
important for trust when the overall risk level of the task is high. We also
present an adaptive strategy for the robot that uses Inverse Reinforcement
Learning (IRL) to match the values of the robot with those of the human during
interaction. Our simulations suggest that such an adaptive strategy is able to
maintain trust across the full spectrum of human values. We also present
results from an empirical study that validate these findings from simulation.
Results indicate that real-time personalized value alignment is beneficial to
trust and perceived performance by the human when the robot does not have a
good prior on the human's values.
