# PyRep: Bringing V-REP to Deep Robot Learning

arXiv: https://arxiv.org/abs/1906.11176 · HTML: https://mlsanity.com/paper/1906.11176/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2019.

## Built on by (most cited first, 30 of 44)

- [Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation](https://mlsanity.com/paper/2209.05451/) (2022)
- [Language-Conditioned Imitation Learning for Robot Manipulation Tasks](https://mlsanity.com/paper/2010.12083/) (2020)
- [THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation](https://mlsanity.com/paper/2402.08191/) (2024)
- [Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulation](https://mlsanity.com/paper/2105.14829/) (2021)
- [Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications](https://mlsanity.com/paper/2510.07077/) (2025)
- [VLMbench: A Compositional Benchmark for Vision-and-Language Manipulation](https://mlsanity.com/paper/2206.08522/) (2022)
- [RoboVerse: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning](https://mlsanity.com/paper/2504.18904/) (2025)
- [Fourier Transporter: Bi-Equivariant Robotic Manipulation in 3D](https://mlsanity.com/paper/2401.12046/) (2024)
- [The Logical Options Framework](https://mlsanity.com/paper/2102.12571/) (2021)
- [Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics](https://mlsanity.com/paper/2107.09822/) (2021)
- [Generative Image as Action Models](https://mlsanity.com/paper/2407.07875/) (2024)
- [Continuous Control with Coarse-to-fine Reinforcement Learning](https://mlsanity.com/paper/2407.07787/) (2024)
- [Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation](https://mlsanity.com/paper/2406.09738/) (2024)
- [Easier Said Than Done: Unpacking Intent-Behavior Gap in Jailbreaking LLM-Based Robots](https://mlsanity.com/paper/2412.16633/) (2024)
- [When to Update Your Model: Constrained Model-based Reinforcement Learning](https://mlsanity.com/paper/2210.08349/) (2022)
- [GLOVER++: Unleashing the Potential of Affordance Learning from Human Behaviors for Robotic Manipulation](https://mlsanity.com/paper/2505.11865/) (2025)
- [Language-Conditioned Path Planning](https://mlsanity.com/paper/2308.16893/) (2023)
- [NaturalVLM: Leveraging Fine-grained Natural Language for Affordance-Guided Visual Manipulation](https://mlsanity.com/paper/2403.08355/) (2024)
- [Coarse-to-fine Q-Network with Action Sequence for Data-Efficient Reinforcement Learning](https://mlsanity.com/paper/2411.12155/) (2024)
- [Coarse-to-Fine for Sim-to-Real: Sub-Millimetre Precision Across Wide Task Spaces](https://mlsanity.com/paper/2105.11283/) (2021)
- [Where To Start? Transferring Simple Skills to Complex Environments](https://mlsanity.com/paper/2212.06111/) (2022)
- [Ivy: Templated Deep Learning for Inter-Framework Portability](https://mlsanity.com/paper/2102.02886/) (2021)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [Object and Relation Centric Representations for Push Effect Prediction](https://mlsanity.com/paper/2102.02100/) (2021)
- [Redundancy-aware Action Spaces for Robot Learning](https://mlsanity.com/paper/2406.04144/) (2024)
- [SIMstack: A Generative Shape and Instance Model for Unordered Object Stacks](https://mlsanity.com/paper/2103.16442/) (2021)
- [Colosseum V2: Benchmarking Generalization for Vision-Language-Action Models](https://mlsanity.com/paper/2605.27759/) (2026)
- [End-to-End Egospheric Spatial Memory](https://mlsanity.com/paper/2102.07764/) (2021)
- [A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation](https://mlsanity.com/paper/2102.08863/) (2021)
- [Data-Driven Stochastic Motion Evaluation and Optimization with Image by Spatially-Aligned Temporal Encoding](https://mlsanity.com/paper/2302.05041/) (2023)

## Abstract (arXiv)

PyRep is a toolkit for robot learning research, built on top of the virtual robotics experimentation platform (V-REP). Through a series of modifications and additions, we have created a tailored version of V-REP built with robot learning in mind. The new PyRep toolkit offers three improvements: (1) a simple and flexible API for robot control and scene manipulation, (2) a new rendering engine, and (3) speed boosts upwards of 10,000x in comparison to the previous Python Remote API. With these improvements, we believe PyRep is the ideal toolkit to facilitate rapid prototyping of learning algorithms in the areas of reinforcement learning, imitation learning, state estimation, mapping, and computer vision.