V-REP: A versatile and scalable robot simulation framework

By E. Rohmer, Surya P. N. Singh, Marc Freese. 2013

1,685 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Mechanical Engineering, Robotics & Control

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Vision and Pattern Recognition

In Mechanical Engineering

In Robotics & Control

Citations in the corpus

A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation (2016, neutral): “Modalities Framerate Target Use Case Gazebo (Koenig et al., 2004) Sensor Plugins 10s+FPS General Purposes Vrep (Rohmer et al., 2013) Sensor Plugins 10s+FPS General Purposes Airsim (Shah et al., 2017) Depth/Color/Semantics 20s+FPS Autonomous Driving Carla (Dosovitskiy et al., 2017)…”
Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning (2018, methodology): “We train with prioritized experience replay [38] using stochastic rank-based prioritization, approximated with a power-law distribution.”
RLBench: The Robot Learning Benchmark & Learning Environment (2019, background): “The benchmark and learning environment is built around a CoppeliaSim/V-REP [23] and PyRep [12] interface.”
Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task (2017, methodology): “tages, and henceforth, we refer to the sequence of stages as an episode. At the start of each episode, the cube and basket is placed randomly within an area that is shown in Figure3. We use the V-REP [30] simulator during all data collection. 3 Figure 3: Variations in the positioning of the cube, ba”
Language-Conditioned Imitation Learning for Robot Manipulation Tasks (2020, methodology): “The evaluation was performed using CoppeliaSim [30, 17], which allowed for accurate dynamics simulations at an update rate of 20Hz.”
SAPIEN: A SimulAted Part-Based Interactive ENvironment (2020, background): “…and robot camera views (bottom): RGB image, surface normals, depth and semantic segmentation of motion parts, while a robot is learning to operate a dishwasher. have long been pursuing simulated environments for tasks such as navigation [41, 56, 42, 1, 3, 54, 14, 56] and control [24, 40, 48, 10].”
RVT: Robotic View Transformer for 3D Object Manipulation (2023, methodology): “We follow the simulation setup in PerAct [6], where CoppelaSim [62] is applied to simulate various RLBench [7] tasks.”
Benchmarking Simulated Robotic Manipulation Through a Real World Dataset (2019, methodology): “V-Rep physics engines Bullet and ODE use frictional coefficients of 0.71, Vortex uses a value of 0.5 for the linear primary axis friction and for Newton both the static and kinetic frictions are 0.5.”
A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022, background): “CoppeliaSim/VREP [214] also allows for online mesh manipulation, but it is not an open-source solution and is less integrated into ROS, limiting its adoption.”
A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics (2021, methodology): “Some well-known robotics simulation tools are Gazebo [252], MuJoCo [253], and CoppeliaSim (aka V-REP) [254].”
Aligning Cyber Space With Physical World: A Comprehensive Survey on Embodied AI (2024, neutral): “Embodiedperceptionrequires visualperceptionandreasoning,understandingthe3-Drelations within a scene, and predicting and performing complex tasks based on visual information.”
Why off-the-shelf physics simulators fail in evaluating feedback controller performance - a case study for quadrupedal robots (2016, neutral): “1 Thus many simulators, both open source 2 and commercial 3,4 rely on mature, accurate physics engines such as ODE 5 or Bullet.”
PyRep: Bringing V-REP to Deep Robot Learning (2019, methodology): “V-REP [11] is a general-purpose robot simulation framework maintained by Coppelia Robotics .”
Research and development in agricultural robotics: a perspective of digital farming. (2018, methodology): “In order to experiment with vision sensors and agricultural robots [7] , created a completely simulated environment in V-REP (Coppelia Robotics) [78] , ROS [79] , and MATLAB (Mathworks, Natick, MA, USA) for improvement of plant/fruit scanning and visual servoing task through an easy testing and…”
Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation (2017, methodology): “On the other hand, A3C needs several parallel simulation environments, which limited its extension to some specific simulation engine like V-REP [21] which can not be paralleled in the same machine.”
Synthetic Data for Deep Learning (2019, background): “3) and the V-REP robot simulation framework [496] that not only provided visual information but also simulated a number of actual robot types, further simplifying control deployment and development.”
The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark Towards Physically Realistic Embodied AI (2021, methodology): “There are several stand-alone physics engines widely used in the robotics community, including PyBullet [12], MuJuCo [38], and V-REP [29].”
Learning Visual Affordances for Robotic Manipulation (2019, methodology): “Our simulation setup uses a UR5 robot arm with an RG2 gripper in V-REP [25] (illustrated in Fig.”
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning (2021, methodology): “Popular physics engines like MuJoCo[30], PyBullet[6], DART[12], Drake[28], V-Rep[25] etc.”