mjorbit: A Simulation Framework for Space Robotics

• URL: http://arxiv.org/abs/2609.08010v1
• Date: Mon, 07 Sep 2026 21:45:19 GMT
• Title: mjorbit: A Simulation Framework for Space Robotics
• Abstract summary: This paper presents a general framework for simulating multi-body space robots with contact.<n>We bring efficient, large-scale robot simulation to in-space servicing, assembly, and manufacturing applications.<n>We present mjorbit, a general, flexible, and performant framework built on the MuJoCo engine widely used in robotics.
• Score: 9.58318935962458
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a general framework for simulating multi-body space robots with contact. We bring efficient, large-scale robot simulation to in-space servicing, assembly, and manufacturing applications. First, we perform an empirical trade study of methods for coupling orbit propagation with existing robotics simulation frameworks. Next, we present mjorbit, a general, flexible, and performant framework built on the MuJoCo engine widely used in robotics, to which we add key spacecraft dynamics, actuators, and sensors. We provide a low-latency C++ CPU backend and a high-throughput GPU backend behind a simple Python API. We demonstrate mjorbit by solving several realistic on-orbit case studies with both model-predictive control and reinforcement learning. Open-source code and examples are available at: https://johnzhang3.github.io/mjorbit/

Related papers

• OASIS: From Simulation Data Collection to Real-World Humanoid Loco-Manipulation [51.37436817948304]
  OASIS is a simulation-data-driven framework for humanoid loco-manipulation.<n>It reconstructs realistic object assets from real-world images using a 3D generative model.<n>Under zero-shot deployment, it achieves higher success rates on most tasks than that trained on real-robot teleoperation data.
  arXiv  Detail & Related papers  (2026-06-07T10:01:53Z)
• Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning [72.43357471969564]
  Isaac Lab combines high-fidelity GPU parallel physics, rendering, and a modular, composable architecture for designing environments and training robot policies.<n>We highlight its application to a diverse set of challenges, including whole-body control, cross-embodiment mobility, contact-rich and dexterous manipulation, and the integration of human demonstrations for skill acquisition.<n>We believe Isaac Lab's combination of advanced simulation capabilities, rich sensing, and data-center scale execution will help unlock the next generation of breakthroughs in robotics research.
  arXiv  Detail & Related papers  (2025-11-06T21:43:02Z)
• GaussGym: An open-source real-to-sim framework for learning locomotion from pixels [78.05453137978132]
  We present a novel approach for photorealistic robot simulation that integrates 3D Gaussian Splatting as a drop-in within vectorized physics simulators.<n>This enables unprecedented speed -- exceeding 100,000 steps per second on consumer GPU.<n>We additionally demonstrate its applicability in a sim-to-real robotics setting.
  arXiv  Detail & Related papers  (2025-10-17T06:34:52Z)
• Robot Control Stack: A Lean Ecosystem for Robot Learning at Scale [11.166320712764465]
  Vision-Language-Action models (VLAs) replace specialized architectures and task-tailored components of expert policies with large-scale data collection and setup-specific fine-tuning.<n>Traditional robotics software frameworks become a bottleneck, while robot simulations offer only limited support for transitioning from and to real-world experiments.<n>We introduce Robot Control Stack (RCS), a lean ecosystem designed from the ground up to support research in robot learning with large-scale generalist policies.
  arXiv  Detail & Related papers  (2025-09-18T13:12:16Z)
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots [133.23509142762356]
  General-purpose robots need a versatile body and an intelligent mind.<n>Recent advancements in humanoid robots have shown great promise as a hardware platform for building generalist autonomy.<n>We introduce GR00T N1, an open foundation model for humanoid robots.
  arXiv  Detail & Related papers  (2025-03-18T21:06:21Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot Interaction [17.093672006793984]
  We present the ROS-PyBullet Interface, a framework that provides a bridge between the reliable contact/impact simulator PyBullet and the Robot Operating System (ROS) Furthermore, we provide additional utilities for facilitating Human-Robot Interaction (HRI) in the simulated environment.
  arXiv  Detail & Related papers  (2022-10-13T10:31:36Z)
• Factory: Fast Contact for Robotic Assembly [29.948128168543114]
  Factory is a set of physics simulation methods and robot learning tools. We achieve real-time or faster simulation of a wide range of contact-rich scenes. We provide $60$ carefully-designed part models, 3 robotic assembly environments, and 7 robot controllers for training and testing virtual robots.
  arXiv  Detail & Related papers  (2022-05-07T03:27:30Z)
• A Framework for Learning Predator-prey Agents from Simulation to Real World [0.0]
  We propose an evolutionary predatorprey robot system which can be generally implemented from simulation to the real world. Both the predators and prey are co-evolved by NeuroEvolution of Augmenting Topologies (NEAT) to learn the expected behaviours. For the convenience of users, the source code and videos of the simulated and real world are published on Github.
  arXiv  Detail & Related papers  (2020-10-29T17:33:38Z)
• Deep Imitation Learning for Bimanual Robotic Manipulation [70.56142804957187]
  We present a deep imitation learning framework for robotic bimanual manipulation. A core challenge is to generalize the manipulation skills to objects in different locations. We propose to (i) decompose the multi-modal dynamics into elemental movement primitives, (ii) parameterize each primitive using a recurrent graph neural network to capture interactions, and (iii) integrate a high-level planner that composes primitives sequentially and a low-level controller to combine primitive dynamics and inverse kinematics control.
  arXiv  Detail & Related papers  (2020-10-11T01:40:03Z)
• robo-gym -- An Open Source Toolkit for Distributed Deep Reinforcement Learning on Real and Simulated Robots [0.5161531917413708]
  We propose an open source toolkit: robo-gym to increase the use of Deep Reinforcement Learning with real robots. We demonstrate a unified setup for simulation and real environments which enables a seamless transfer from training in simulation to application on the robot. We showcase the capabilities and the effectiveness of the framework with two real world applications featuring industrial robots.
  arXiv  Detail & Related papers  (2020-07-06T13:51:33Z)

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.