Home-service robots are expected to perform a wide range of tasks commonly encountered in a household environment. For autonomous operations robots should be able to plan their actions to carry out these tasks in advance and they should at least have the ability to plan for additional tasks during their operation. Because of the variability and uncertainty in the environment, it is best to endow robots with a learning-based task planning capability that rests on human-robot interaction (HRI). We take a case-based reasoning (CBR) approach to home-service-robot learning and incorporate the cognitive HRI paradigm that includes four cognitive models (needs, task, interaction, and user model) for case adaptations to the given situation. Given a new command from user, a robot finds the closest task case from already existing tasks to start with a plan and modifies it (i.e. action sequences) to adapt to the given situation based on the cognitive models. In order to promote the reusability and flexibility of task cases used in our CBR approach, a robot task description language (RTDL) is designed to represent tasks using an atomic action taxonomy [1]. The proposed approach is applied to a "Bring me a coke" scenario and implemented in our robot system called IDRO.