Simulately-robotics simulation platform.
AI-powered robotics simulation for research.
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Compare IsaacGym and Pybullet.
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Introduction to Simulately
Simulately is a specialized project focused on consolidating essential resources for physics simulation in robotics. It serves as a comprehensive platform for roboticists and researchers working on robot learning and manipulation. The project aims to provide in-depth information on various simulators, including their features, performance comparisons, and practical implementation details. Simulately also includes a collection of useful code snippets, guides on handling common problems in simulations, and a repository of related work and toolkits to support advanced research in the field. An example scenario illustrating its purpose would be a researcher comparing different physics engines like IsaacGym and PyBullet to determine the best option for simulating a robotic manipulation task.
Main Functions of Simulately
Simulator Comparison
ExampleComparing rendering speed and sensor simulation capabilities across simulators like IsaacGym, SAPIEN, and MuJoCo.
ScenarioA robotics engineer deciding which simulator to use for high-speed robotic arm manipulation tasks would use Simulately to compare performance metrics and select the most suitable simulator.
Snippets and Toolkits
ExampleProviding Python code snippets for tasks such as image processing using OpenCV, or point cloud processing using Open3D.
ScenarioA researcher implementing a custom sensor model in a simulator can use the provided code snippets to efficiently process sensor data and integrate it into their simulation environment.
Dataset and Related Work Collection
ExampleOffering access to 3D object datasets like ShapeNet or YCB, along with papers and code repositories for further study.
ScenarioA PhD student working on object recognition for robotic grasping could use Simulately to quickly find relevant datasets and literature to support their research.
Ideal Users of Simulately
Roboticists and Researchers
Researchers working on robot learning, manipulation, and simulation will benefit from Simulately's detailed simulator comparisons, toolkits, and datasets. These users typically need to choose appropriate simulation tools and require robust resources for implementing and testing their algorithms.
Graduate Students and Academics
Graduate students and academic professionals involved in robotics research can leverage Simulately to access a wide range of educational resources, including tutorials, related work, and prebuilt snippets, which help accelerate their research and development processes.
How to Use Simulately
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Start by accessing Simulately through the website to explore its features without the requirement for a login or a premium account.
Choose Your Simulation Task
Select the type of robotics simulation task you want to perform, such as object manipulation, reinforcement learning, or dataset processing.
Configure Your Simulation Environment
Use Simulately’s interface to set up your simulation environment. Choose the relevant simulators, datasets, and toolkits as per your project requirements.
Run and Monitor Simulations
Initiate your simulation and monitor the results in real-time. Adjust parameters as necessary to refine the outcomes.
Analyze and Export Results
Once the simulation is complete, analyze the data using Simulately's built-in tools, and export the results for further research or implementation.
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Detailed Q&A about Simulately
What is Simulately used for?
Simulately is a tool designed for robotics research, providing a platform to simulate various robotics tasks such as manipulation, reinforcement learning, and more. It integrates multiple simulators and datasets to enhance research and development in robotic learning.
Can Simulately be used for academic purposes?
Yes, Simulately is highly suitable for academic research, offering a rich repository of tools, simulators, and datasets that support cutting-edge research in robotics and machine learning.
What types of simulations can be run on Simulately?
Simulately supports a wide range of simulations including object manipulation, reinforcement learning, physics-based simulations, and robotic vision tasks. It also allows users to customize their simulation environments for specific research needs.
Does Simulately offer support for different simulators?
Yes, Simulately provides support for multiple simulators like IsaacSim, PyBullet, MuJoCo, and others, allowing users to choose the most appropriate tool for their research or project.
How can I contribute to the Simulately project?
You can contribute to Simulately by submitting issues or pull requests via GitHub. The platform encourages contributions related to simulator enhancements, dataset expansion, and the development of new tools and features.