# Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation Source: [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:57:11.341Z Authors: Tianhao Zhang, Zoe McCarthy, Owen Jow, Dennis Lee, Xi Chen, Ken Goldberg, Pieter Abbeel Published: 2017-10-12T17:02:31.000Z ## Abstract Imitation learning is a powerful paradigm for robot skill acquisition. However, obtaining demonstrations suitable for learning a policy that maps from raw pixels to actions can be challenging. In this paper we describe how consumer-grade Virtual Reality headsets and hand tracking hardware can be used to naturally teleoperate robots to perform complex tasks. We also describe how imitation learning can learn deep neural network policies (mapping from pixels to actions) that can acquire the demonstrated skills. Our experiments showcase the effectiveness of our approach for learning visuomotor skills. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Bimanual Insertion on Bimanual Insertion sim](): Bimanual Insertion; Bimanual Insertion sim; Grasp Success: 10. - [Cube Transfer on Cube Transfer sim](): Cube Transfer; Cube Transfer sim; Touched Count: 34. - [Slide Ziploc on Slide Ziploc real](): Slide Ziploc; Slide Ziploc real; Grasp Success: 0. - [Slot Battery on Slot Battery real](): Slot Battery; Slot Battery real; Grasp Success: 0. ## Original sources and implementation [Original paper]() [PDF]()