In collaborative tasks, expectations for achieving shared goals arise at all hierarchical plan levels, including plans, tasks, subtasks, and actions. However, these expectations also generate uncertainties for individuals executing the joint plan. If left unresolved, these uncertainties can impede successful task completion. Uncertainties may relate to the agents' motivation to initiate, continue, or complete their plan (motivational uncertainty), the best way to execute their shared plan (instrumental uncertainty), and their knowledge of other agents and the environment (common ground uncertainty). These expectations can be either normative or descriptive, but only normative expectations trigger reactions from agents to resolve the aforementioned types of uncertainties. Thus, this paper introduces a theoretical model that enables a robot to consider all agents' expectations and take actions that reduce the uncertainties associated with their shared plan. By doing so, we aim to enhance the likelihood of success in joint plans between robots and humans. To demonstrate the effectiveness of our theoretical commitment model, we have implemented a proof of concept for a client service use case in a food shop.