  Aging societies, labor shortages and increasing wage costs call for
assistance robots capable of autonomously performing a wide array of real-world
tasks. Such open-ended robotic manipulation requires not only powerful
knowledge representations and reasoning (KR&R) algorithms, but also methods for
humans to instruct robots what tasks to perform and how to perform them. In
this paper, we present a system for automatically generating executable robot
control programs from human task demonstrations in virtual reality (VR). We
leverage common-sense knowledge and game engine-based physics to semantically
interpret human VR demonstrations, as well as an expressive and general task
representation and automatic path planning and code generation, embedded into a
state-of-the-art cognitive architecture. We demonstrate our approach in the
context of force-sensitive fetch-and-place for a robotic shopping assistant.
The source code is available at
https://github.com/ease-crc/vr-program-synthesis.
