用于航天器交会与接近操作的GPU加速天体动力学世界模型
GPU-Accelerated Astrodynamics World Models for Spacecraft Rendezvous and Proximity Operations
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中文总结 AI 辅助
本文提出一种基于Transformer的世界模型方法，结合GPU加速的ISS对接环境，实现航天器交会与接近操作，在对接成功率、分布外泛化及异常检测上优于基线，已开源相关资源。
中文摘要 AI 辅助
世界模型是表征学习领域中一种新兴范式，智能体可从离线轨迹数据中联合学习状态-动作动力学与观测模型，支持多步规划及带不确定性估计的轨迹预测。该模型在机器人与游戏环境中已展现出优异性能，但据我们所知，此前尚未应用于航天领域。本文提出一种基于世界模型的方法，用于合作与非合作航天器交会及接近操作。首先，我们推出开源的、基于JAX的国际空间站（ISS）对接环境，支持航天器轨道与姿态动力学的并行GPU模拟，可生成世界模型训练所需的数千个状态-动作转换样本。其次，我们提出Out-of-this-World-Model，一种基于Transformer的世界模型，它将相对运动学状态与体固定相机图像编码为隐状态，并利用一步流匹配预测该隐状态在指令推力与扭矩作用下的演化过程。该模型可生成未来观测的分布，捕捉随机动力学与每一步的不确定性，且相比DreamerV3风格的后验校正基线，它拥有更少的可训练参数与超参数，性能更优。第三，我们将该方法应用于在禁区约束下与ISS自主对接的舱段，相较于强化学习基线，该方法展现出更高的样本效率与任务性能（各对接端口的对接成功率为53%，而基线为29%）、更优的分布外泛化能力（在预留端口上，世界模型的成功率较基线翻倍以上，达40%，而基线为17%），且对接近过程中遇到的异常物体的分类准确率达98%。我们开源了该模拟环境与模型架构，以支持对该范式的进一步研究。
英文摘要
World models are an emerging paradigm in representation learning in which an agent jointly learns state-action dynamics and observation models from offline trajectory data, enabling multi-step planning and trajectory prediction with uncertainty estimates. They have shown strong results in robotics and game environments, but, to the best of our knowledge, have not previously been applied to the space domain. This paper introduces a world model-based approach to cooperative and non-cooperative spacecraft rendezvous and proximity operations. First, we introduce an open-source, JAX-based International Space Station (ISS) docking environment supporting parallel GPU simulation of spacecraft orbit and attitude dynamics, generating the thousands of state-action transitions that world model training requires. Second, we introduce Out-of-this-World-Model, a transformer-based world model that encodes relative kinematic states and body-fixed camera imagery into a latent state and predicts its evolution under commanded thrusts and torques using one-step flow matching. It produces a distribution over future observations, capturing stochastic dynamics and per-timestep uncertainty, and outperforms DreamerV3-style posterior-correction baselines with fewer trainable parameters and hyperparameters. Third, we apply the approach to a capsule autonomously docking with the ISS under keep-out-zone constraints, demonstrating improved sample efficiency and task performance over reinforcement learning baselines (53% versus 29% docking success across ports), better out-of-distribution generalization (on held-out ports the world model more than doubles baseline success, 40% versus 17%), and detection of anomalous objects encountered during approach with 98% classification accuracy. We open-source the simulation environment and model architecture to enable further study of this paradigm.
发表机构
- Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。
