Omnichannel services, such as buy-online-pickup-in-store, curbside pickup, and ship-from-store, have shifted the order-picking tasks previously completed by in-store customers doing their own shopping to the retailer’s responsibility. To fulfill these orders, many retailers have deployed a store fulfillment strategy, where online orders are picked from brick-and-mortar retail store shelves. We focus on the design of operations inside a store where in-store customers collaborate with autonomous mobile robots (AMRs) to pick online orders. Due to the uncertainty in in-store customers’ availability and their willingness to participate, the problem of synchronizing in-store customers with AMRs is highly stochastic. Thus, we model the stochastic order-picking problem with uncertain synchronization times of in-store customers and AMRs as a Markov Decision Process to determine how a retailer should dynamically assign tasks to a set of AMRs and dedicated pickers. We develop a heuristic solution framework that generates a set of initial assignments and routes for heterogenous picking resources and dynamically updates them as the actual synchronization times between AMRs and in-store customers unfold. We analyze multiple strategies to generate the initial set of task assignments and routes as well as update such decisions based on the system state. To provide guidance on whether the proposed approach is economically and operationally viable, we conduct extensive computational experiments using actual online grocery orders and empirical shopping behavior data. We illustrate the feasibility of such a policy to achieve similar picking performance as the status quo and through an economic analysis show that deploying dedicated pickers and AMRs aided by in-store customers in a store environment is economically viable.