A Comparison of Humanoid Robot Simulators: A Quantitative Approach
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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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Aug 2020.
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Citing papers per year: 2021: 1; 2022: 4; 2023: 2; 2024: 0; 2025: 0; 2026: 1.
8 papers.
- 2021Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement Learningcited by 10
- 2022Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarioscited by 5
- 2023Facilitating Sim-to-real by Intrinsic Stochasticity of Real-Time Simulation in Reinforcement Learning for Robot Manipulationcited by 2
- 2023Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversitycited by 2
- 2022Broad-persistent Advice for Interactive Reinforcement Learning Scenarioscited by 1
- 2022Reinforcement Learning for UAV control with Policy and Reward Shapingcited by 1
- 2022Introspection-based Explainable Reinforcement Learning in Episodic and Non-episodic Scenarioscited by 0
- 2026Self-Healing Visual Recovery for Autonomous Ground Vehicles Using Camera-Only Visual Odometrycited by 0
- 2026Self-Healing Visual Recovery for Autonomous Ground Vehicles Using Camera-Only Visual Odometry13 Jul 2026
- 2023Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity7 May 2023
- 2023Facilitating Sim-to-real by Intrinsic Stochasticity of Real-Time Simulation in Reinforcement Learning for Robot Manipulation12 Apr 2023
- 2022Reinforcement Learning for UAV control with Policy and Reward Shaping6 Dec 2022
- 2022Introspection-based Explainable Reinforcement Learning in Episodic and Non-episodic Scenarios23 Nov 2022
- 2022Broad-persistent Advice for Interactive Reinforcement Learning Scenarios11 Oct 2022
- 2022Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios7 Jul 2022
- 2021Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement Learning8 Mar 2021