# OpenVR: Teleoperation for Manipulation

arXiv: https://arxiv.org/abs/2305.09765 · HTML: https://mlsanity.com/paper/2305.09765/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 46 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 18 of 18)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [Time-Contrastive Networks: Self-Supervised Learning from Video](https://mlsanity.com/paper/1704.06888/) (2017)
- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation](https://mlsanity.com/paper/1811.02790/) (2018)
- [Human-in-the-Loop Imitation Learning using Remote Teleoperation](https://mlsanity.com/paper/2012.06733/) (2020)
- [Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration](https://mlsanity.com/paper/2105.06411/) (2021)
- [Robotic Telekinesis: Learning a Robotic Hand Imitator by Watching Humans on Youtube](https://mlsanity.com/paper/2202.10448/) (2022)
- [Holo-Dex: Teaching Dexterity with Immersive Mixed Reality](https://mlsanity.com/paper/2210.06463/) (2022)
- [A System for Imitation Learning of Contact-Rich Bimanual Manipulation Policies](https://mlsanity.com/paper/2208.00596/) (2022)
- [A Modular Robotic Arm Control Stack for Research: Franka-Interface and FrankaPy](https://mlsanity.com/paper/2011.02398/) (2020)
- [Unified Learning from Demonstrations, Corrections, and Preferences during Physical Human-Robot Interaction](https://mlsanity.com/paper/2207.03395/) (2022)
- [Learning from Sparse Demonstrations](https://mlsanity.com/paper/2008.02159/) (2020)
- [Learning Forceful Manipulation Skills from Multi-modal Human Demonstrations](https://mlsanity.com/paper/2109.04222/) (2021)
- [Minimizing Human Assistance: Augmenting a Single Demonstration for Deep Reinforcement Learning](https://mlsanity.com/paper/2209.11275/) (2022)
- [Creativity in Robot Manipulation with Deep Reinforcement Learning](https://mlsanity.com/paper/1910.07459/) (2019)
- [MAN: Multi-Action Networks Learning](https://mlsanity.com/paper/2209.09329/) (2022)

## Built on by (most cited first, 8 of 8)

- [EgoMimic: Scaling Imitation Learning via Egocentric Video](https://mlsanity.com/paper/2410.24221/) (2024)
- [One ACT Play: Single Demonstration Behavior Cloning with Action Chunking Transformers](https://mlsanity.com/paper/2309.10175/) (2023)
- [VITaL Pretraining: Visuo-Tactile Pretraining for Tactile and Non-Tactile Manipulation Policies](https://mlsanity.com/paper/2403.11898/) (2024)
- [SculptBot: Pre-Trained Models for 3D Deformable Object Manipulation](https://mlsanity.com/paper/2309.08728/) (2023)
- [ARCADE: Scalable Demonstration Collection and Generation via Augmented Reality for Imitation Learning](https://mlsanity.com/paper/2410.15994/) (2024)
- [IRIS: An Immersive Robot Interaction System](https://mlsanity.com/paper/2502.03297/) (2025)
- [LeVR: A Modular VR Teleoperation Framework for Imitation Learning in Dexterous Manipulation](https://mlsanity.com/paper/2509.14349/) (2025)
- [iFlyBot-VLA Technical Report](https://mlsanity.com/paper/2511.01914/) (2025)

## Abstract (arXiv)

Across the robotics field, quality demonstrations are an integral part of many control pipelines. However, collecting high-quality demonstration trajectories remains time-consuming and difficult, often resulting in the number of demonstrations being the performance bottleneck. To address this issue, we present a method of Virtual Reality (VR) Teleoperation that uses an Oculus VR headset to teleoperate a Franka Emika Panda robot. Although other VR teleoperation methods exist, our code is open source, designed for readily available consumer hardware, easy to modify, agnostic to experimental setup, and simple to use.