# Space Robotics Bench: Robot Learning Beyond Earth

arXiv: https://arxiv.org/abs/2509.23328 · HTML: https://mlsanity.com/paper/2509.23328/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 42 references that are in the map.
- First posted on arXiv 27 Sep 2025.

## Builds on (most cited first, 21 of 21)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [RLBench: The Robot Learning Benchmark & Learning Environment](https://mlsanity.com/paper/1909.12271/) (2019)
- [robosuite: A Modular Simulation Framework and Benchmark for Robot Learning](https://mlsanity.com/paper/2009.12293/) (2020)
- [Leveraging Procedural Generation to Benchmark Reinforcement Learning](https://mlsanity.com/paper/1912.01588/) (2019)
- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation](https://mlsanity.com/paper/2305.12821/) (2023)
- [HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation](https://mlsanity.com/paper/2403.10506/) (2024)
- [Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning](https://mlsanity.com/paper/2501.02116/) (2025)
- [skrl: Modular and Flexible Library for Reinforcement Learning](https://mlsanity.com/paper/2202.03825/) (2022)
- [RoboHive: A Unified Framework for Robot Learning](https://mlsanity.com/paper/2310.06828/) (2023)
- [RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning](https://mlsanity.com/paper/2304.04150/) (2023)
- [FetchBench: A Simulation Benchmark for Robot Fetching](https://mlsanity.com/paper/2406.11793/) (2024)
- [Learning to Grasp on the Moon from 3D Octree Observations with Deep Reinforcement Learning](https://mlsanity.com/paper/2208.00818/) (2022)
- [RANS: Highly-Parallelised Simulator for Reinforcement Learning based Autonomous Navigating Spacecrafts](https://mlsanity.com/paper/2310.07393/) (2023)
- [MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated Environments](https://mlsanity.com/paper/2107.09996/) (2021)
- [OmniLRS: A Photorealistic Simulator for Lunar Robotics](https://mlsanity.com/paper/2309.08997/) (2023)

## Built on by (most cited first, 4 of 4)

- [Learning Adaptive Multi-Task Guidance, Navigation, and Control via Hypernetworks](https://mlsanity.com/paper/2607.24292/) (2026)
- [Orbit-Planner: Towards Latent World Models for On-Orbit Obstacle Avoidance of Satellite Agents](https://mlsanity.com/paper/2608.16651/) (2026)
- [Reward-Free Continual Adaptation for Resilient Space Robots](https://mlsanity.com/paper/2608.23452/) (2026)
- [A Minimal Optical-Flow Representation for Vision-Based Tactile Rotation Classification in Robotic Manipulation Across Gravity Domains](https://mlsanity.com/paper/2610.12073/) (2026)

## Abstract (arXiv)

The growing ambition for space exploration demands robust autonomous systems that can operate in unstructured environments under extreme extraterrestrial conditions. The adoption of robot learning in this domain is severely hindered by the prohibitive cost of technology demonstrations and the limited availability of data. To bridge this gap, we introduce the Space Robotics Bench, an open-source simulation framework for robot learning in space. It offers a modular architecture that integrates on-demand procedural generation with massively parallel simulation environments to support the creation of vast and diverse training distributions for learning-based agents. To ground research and enable direct comparison, the framework includes a comprehensive suite of benchmark tasks that span a wide range of mission-relevant scenarios. We establish performance baselines using standard reinforcement learning algorithms and present a series of experimental case studies that investigate key challenges in generalization, end-to-end learning, adaptive control, and sim-to-real transfer. Our results reveal insights into the limitations of current methods and demonstrate the utility of the framework in producing policies capable of real-world operation. These contributions establish the Space Robotics Bench as a valuable resource for developing, benchmarking, and deploying the robust autonomous systems required for the final frontier.