Autonomous manipulation robots can be valuable aids as interactive agents in the home, yet it has proven extremely difficult to program their behavior. Imitation learning uses data on human demonstrations to build behavioral models for robots. In order to cover a wide range of action strategies, data from many individuals is needed. Acquiring such large amounts of data can be a challenge. Tools for data capturing in this domain must thus implement a good user experience. We propose to use human computation games in order to gather data on human manual behavior. We demonstrate the idea with a strategy game that is operated via a natural user interface. A comparison between using the game for action execution and demonstrating actions in a virtual environment shows that people interact longer and have a better experience when playing the game.