Typically, humans interact with a humanoid robot with apprehension. This lack of trust can seriously affect the effectiveness of a team of robots and humans. We can create effective interactions that generate trust by augmenting robots with an explanation capability. The explanations provide justification and transparency to the robot’s decisions. To demonstrate such effective interaction, we tested this with an interactive, game-playing environment with partial information that requires team collaboration, using a game called Spanish Domino. We partner a robot with a human to form a pair, and this team opposes a team of two humans. We performed a user study with sixty-three human participants in different settings, investigating the effect of the robot’s explanations on the humans’ trust and perception of the robot’s behaviour. Our explanation-generation mechanism produces natural-language sentences that translate the decision taken by the robot into humanunderstandable terms. We video-recorded all interactions to analyse factors such as the participants’ relational behaviours with the robot, and we also used questionnaires to measure the participants’ explicit trust in the robot. Overall, our main results demonstrate that explanations enhanced the participants’ understandability of the robot’s decisions, because we observed a significant increase in the participants’ level of trust in their robotic partner. These results suggest that explanations, stating the reason(s) for a decision, combined with the transparency of the decision-making process, facilitate collaborative human–humanoid interactions.