  This paper applies the principles of Virtual Reality (VR) to robots, rather
than living organisms. A simulator, of either physical states or information
states, renders outputs to custom displays that fool the robot's sensors. This
enables a robot to experience a combination of real and virtual sensor inputs,
combining the efficiency of simulation and the benefits of real world sensor
inputs. Thus, the robot can be taken through targeted experiences that are more
realistic than pure simulation, yet more feasible and controllable than pure
real-world experiences. We define two distinctive methods for applying VR to
robots, namely black box and white box; based on these methods we identify
potential applications, such as testing and verification procedures that are
better than simulation, the study of spoofing attacks and anti-spoofing
techniques, and sample generation for machine learning. A general mathematical
framework is presented, along with a simple experiment, detailed examples, and
discussion of the implications.
