  Theory of Mind (ToM) is a fundamental cognitive architecture that endows
humans with the ability to attribute mental states to others. Humans infer the
desires, beliefs, and intentions of others by observing their behavior and, in
turn, adjust their actions to facilitate better interpersonal communication and
team collaboration. In this paper, we investigated trust-aware robot policy
with the theory of mind in a multiagent setting where a human collaborates with
a robot against another human opponent. We show that by only focusing on team
performance, the robot may resort to the reverse psychology trick, which poses
a significant threat to trust maintenance. The human's trust in the robot will
collapse when they discover deceptive behavior by the robot. To mitigate this
problem, we adopt the robot theory of mind model to infer the human's trust
beliefs, including true belief and false belief (an essential element of ToM).
We designed a dynamic trust-aware reward function based on different trust
beliefs to guide the robot policy learning, which aims to balance between
avoiding human trust collapse due to robot reverse psychology. The experimental
results demonstrate the importance of the ToM-based robot policy for
human-robot trust and the effectiveness of our robot ToM-based robot policy in
multiagent interaction settings.
