Space Robotics Bench (SRB) is a comprehensive collection of environments and tasks for robotics research in the challenging domain of space. It provides a unified framework for developing and validating autonomous systems under diverse extraterrestrial scenarios. At the same time, its design is flexible and extensible to accommodate a variety of development workflows and research directions beyond Earth.
Key Features
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Highly Parallelized Simulation via NVIDIA Isaac Sim: SRB supports thousands of parallel simulation instances to accelerate workflows such as online learning, synthetic dataset generation, parameter tuning, and validation.
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On-Demand Procedural Generation with SimForge: Automated procedural generation of simulation assets is leveraged to provide a unique scenario for each simulation instance, with the ultimate goal of developing autonomous systems that are both robust and adaptable to the unpredictable domain of space.
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Extensive Domain Randomization: All simulation instances can be further randomized to enhance the generalization of autonomous agents towards variable environment dynamics, visual appearance, illumination conditions, as well as sensor and actuation noise.
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Compatibility with Gymnasium API: All tasks are compatible with a standardized API to ensure seamless integration with a broad ecosystem of libraries and frameworks for robot learning research.
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Seamless Interface with ROS 2 & Space ROS: Simulation states, sensory outputs and actions of autonomous systems are available through ROS 2 middleware interface, enabling direct interoperability with the vast (Space) ROS ecosystem.
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Abstract Architecture: The architecture of SRB is designed to be modular and extensible, allowing for easy integration of new assets, robots, tasks and workflows
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