Abstract We will demonstrate a tabletop robotic agent that learns new tasks through interactive natural language instruction. The tasks to be demonstrated are simple puzzles and games, such as Tower of Hanoi, Eight Puzzle, Tic-Tac-Toe, Three Men’s Morris, and the Frog and Toads puzzle. We will include a live, interactive simulation of a mobile robot that learns new tasks using the same system. Humans are not limited to a fixed set of innate or preprogrammed tasks. We quickly learn novel tasks through instruction. We learn to play new games and puzzles in just a few minutes and we can learn navigation and manipulation tasks such as delivering or fetching a package. We are headed to a future populated with autonomous systems that have the cognitive and physical capabilities to perform a wide variety of tasks; however, today we rely on either hand programming or extensive training to teach these systems new tasks. Consider a future where it is possible to directly instruct agents with new tasks in real time using language. This would greatly in-crease the ability of non-experts to extend and customize the computational systems they interact with every day. Our demonstration is centered on the Rosie system [Mohan et al., 2012; Kirk and Laird, 2014] developed in Soar [Laird, 2012] that is embodied in both a tabletop robot and a mobile robot. In the tabletop robot, Rosie learns simple puzzles and games, as well as object manipulation tasks that mirror simple kitchen-like activities. In the mobile robot, it learns tas that involve simple navigation, manipulation, and communication. The agent can learn the tasks from scratch, including termination/goal conditions, legal actions, and task-specific concepts that are grounded in its perceptual and functional primitives. It also transfers learned knowledge to other tasks, such as the concept of three-in-a-row, which can be learned for Tic-Tac-Toe and then used in Three Men’s Morris. If a new concept is used, such as when the agent is taught an action for Othello: ‘If the locations between a clear locationand a captured location are occupied then you can place a piece on the clear location.’, it will request definitions of aundefined words, such as ‘clear’, ‘captured’, and ‘occupied’. The instructor can then provide a definition, such as ‘If a lo-cation is below an enemy piece then the location is occupied.’ We have made the following extensions to Rosie: