Space Robotics Bench: Robot Learning Beyond Earth

• URL: http://arxiv.org/abs/2509.23328v1
• Date: Sat, 27 Sep 2025 14:28:31 GMT
• Title: Space Robotics Bench: Robot Learning Beyond Earth
• Authors: Andrej Orsula, Matthieu Geist, Miguel Olivares-Mendez, Carol Martinez,
• Abstract summary: Space Robotics Bench is an open-source simulation framework for robot learning in space.<n>It integrates on-demand procedural generation with massively parallel simulation environments.<n>It includes a comprehensive suite of benchmark tasks that span a wide range of mission-relevant scenarios.
• Score: 16.948852537273655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation [56.30931340537373]
  MolmoSpaces is a fully open ecosystem to support benchmarking of robot policies.<n>MolmoSpaces consists of over 230k diverse indoor environments.<n>MolmoSpaces-Bench is a benchmark suite of 8 tasks in which robots interact with our diverse scenes and richly annotated objects.
  arXiv  Detail & Related papers  (2026-02-11T20:16:31Z)
• Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence [54.91177026001217]
  Large language model (LLM)-enabled teaming methods typically assume well-structured and known environments.<n>We present SPINE-HT, a framework that addresses these limitations by grounding the reasoning abilities of LLMs in the context of a heterogeneous robot team.<n>Our framework achieves nearly twice the success rate compared to prior LLM-enabled heterogeneous teaming approaches.
  arXiv  Detail & Related papers  (2025-10-30T18:24:38Z)
• Learning Tool-Aware Adaptive Compliant Control for Autonomous Regolith Excavation [16.948852537273655]
  This work introduces a framework where a model-based reinforcement learning agent learns within a parallelized simulation.<n>We show that training with a procedural distribution of tools is critical for generalization and enables the development of sophisticated tool-aware behavior.
  arXiv  Detail & Related papers  (2025-09-05T20:09:28Z)
• RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation [15.548637925166986]
  We present a multi-domain framework for training, evaluating and deploying RL-based navigation policies across diverse robotic platforms and operational environments.<n>Our work presents four key contributions: (1) a scalable and modular framework, facilitating seamless robot-task interchangeability and reproducible training pipelines; (2) sim-to-real transfer demonstrated through real-world experiments with multiple robots; and (3) the release of the first open-source API for deploying Isaac Lab-trained policies to real robots.
  arXiv  Detail & Related papers  (2025-05-20T15:48:23Z)
• A General Infrastructure and Workflow for Quadrotor Deep Reinforcement Learning and Reality Deployment [48.90852123901697]
  We propose a platform that enables seamless transfer of end-to-end deep reinforcement learning (DRL) policies to quadrotors.<n>Our platform provides rich types of environments including hovering, dynamic obstacle avoidance, trajectory tracking, balloon hitting, and planning in unknown environments.
  arXiv  Detail & Related papers  (2025-04-21T14:25:23Z)
• Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics [50.191655141020505]
  This work advances model-based reinforcement learning by addressing the challenges of long-horizon prediction, error accumulation, and sim-to-real transfer.<n>By providing a scalable and robust framework, the introduced methods pave the way for adaptive and efficient robotic systems in real-world applications.
  arXiv  Detail & Related papers  (2025-01-17T10:39:09Z)
• Foundation Models for Autonomous Robots in Unstructured Environments [15.517532442044962]
  The study systematically reviews application of foundation models in two field of robotic and unstructured environment. Findings showed that linguistic capabilities of LLMs have been utilized more than other features for improving perception in human-robot interactions. The use of LLMs demonstrated more applications in project management and safety in construction, and natural hazard detection in disaster management.
  arXiv  Detail & Related papers  (2024-07-19T13:26:52Z)
• Leveraging Procedural Generation for Learning Autonomous Peg-in-Hole Assembly in Space [19.76267553432585]
  We present a novel approach for learning autonomous peg-in-hole assembly in the context of space robotics. Our focus is on enhancing the generalization and adaptability of autonomous systems through deep reinforcement learning. Our findings set the stage for future advancements in intelligent robotic systems capable of supporting ambitious space missions and infrastructure development beyond Earth.
  arXiv  Detail & Related papers  (2024-05-02T09:50:01Z)
• Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis [82.59451639072073]
  General-purpose robots operate seamlessly in any environment, with any object, and utilize various skills to complete diverse tasks. As a community, we have been constraining most robotic systems by designing them for specific tasks, training them on specific datasets, and deploying them within specific environments. Motivated by the impressive open-set performance and content generation capabilities of web-scale, large-capacity pre-trained models, we devote this survey to exploring how foundation models can be applied to general-purpose robotics.
  arXiv  Detail & Related papers  (2023-12-14T10:02:55Z)
• Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents [61.36681529571202]
  We describe a new concept for reproducible robotics research that integrates development and benchmarking. One of the central components of this setup is the Duckietown Autolab, a standardized setup that is itself relatively low-cost and reproducible. We validate the system by analyzing the repeatability of experiments conducted using the infrastructure and show that there is low variance across different robot hardware and across different remote labs.
  arXiv  Detail & Related papers  (2020-09-09T15:31:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.