# Virtual Reality for Robots

arXiv: https://arxiv.org/abs/1909.07096 · HTML: https://mlsanity.com/paper/1909.07096/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2019.

## Built on by (most cited first, 2 of 2)

- [The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning](https://mlsanity.com/paper/2405.03164/) (2024)
- [An Enactivist-Inspired Mathematical Model of Cognition](https://mlsanity.com/paper/2206.06096/) (2022)

## Abstract (arXiv)

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.