In physical interactions, humans naturally monitor the pace and workload of their partners and adapt their handovers accordingly. In this project, we investigated how robots designed to engage in physical collaborations may achieve similar adaptivity in performing handovers. To that end, we collected and analyzed data from human dyads performing a common household task—unloading a dish rack—where receivers had different levels of task demands. We identiﬁed two coordination strategies that enabled givers to adapt to receivers’ task demands. We then formulated and implemented these strategies on a robotic manipulator. The implemented autonomous system was evaluated in a human-robot interaction study against two baselines that use “proactive” and “reactive” coordination methods. The results show a tradeoff between team performance and user experience when human receivers had greater task demands. In particular, the proactive method provided the greatest levels of team performance but offered the poorest user experience compared to the reactive and adaptive methods. The reactive method, while improving user experience over the proactive method, resulted in the poorest team performance. Our adaptive method maintained this improved user experience while offering an improved team performance compared to the reactive method. Our ﬁndings offer insights into the tradeoffs involved in the use of these methods and inform the future design of handover interactions for robots.