# A Comparison of Humanoid Robot Simulators: A Quantitative Approach

arXiv: https://arxiv.org/abs/2008.04627 · HTML: https://mlsanity.com/paper/2008.04627/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2020.

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

- [Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement Learning](https://mlsanity.com/paper/2103.04616/) (2021)
- [Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios](https://mlsanity.com/paper/2207.03214/) (2022)
- [Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity](https://mlsanity.com/paper/2305.04180/) (2023)
- [Facilitating Sim-to-real by Intrinsic Stochasticity of Real-Time Simulation in Reinforcement Learning for Robot Manipulation](https://mlsanity.com/paper/2304.06056/) (2023)
- [Broad-persistent Advice for Interactive Reinforcement Learning Scenarios](https://mlsanity.com/paper/2210.05187/) (2022)
- [Reinforcement Learning for UAV control with Policy and Reward Shaping](https://mlsanity.com/paper/2212.03828/) (2022)
- [Introspection-based Explainable Reinforcement Learning in Episodic and Non-episodic Scenarios](https://mlsanity.com/paper/2211.12930/) (2022)
- [Self-Healing Visual Recovery for Autonomous Ground Vehicles Using Camera-Only Visual Odometry](https://mlsanity.com/paper/2607.11686/) (2026)

## Abstract (arXiv)

Research on humanoid robotic systems involves a considerable amount of computational resources, not only for the involved design but also for its development and subsequent implementation. For robotic systems to be implemented in real-world scenarios, in several situations, it is preferred to develop and test them under controlled environments in order to reduce the risk of errors and unexpected behavior. In this regard, a more accessible and efficient alternative is to implement the environment using robotic simulation tools. This paper presents a quantitative comparison of Gazebo, Webots, and V-REP, three simulators widely used by the research community to develop robotic systems. To compare the performance of these three simulators, elements such as CPU, memory footprint, and disk access are used to measure and compare them to each other. In order to measure the use of resources, each simulator executes 20 times a robotic scenario composed by a NAO robot that must navigate to a goal position avoiding a specific obstacle. In general terms, our results show that Webots is the simulator with the lowest use of resources, followed by V-REP, which has advantages over Gazebo, mainly because of the CPU use.