发表机构
JAXA（日本宇宙航空研究开发机构）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究轨道环境中自主自由飞行机器人多任务控制问题，提出HYPER - GNC框架，通过超网络映射任务嵌入到共享策略权重，使单个控制器掌握多种任务，实验证明其样本效率高且能保持稳定，还弥合了模拟到现实的差距。
AI 中文摘要
轨道环境中的自主自由飞行机器人需要通用且资源高效的控制器，但为每个任务配置文件维护单独的、特定于任务的策略在架构上很脆弱，且随着需求的演变会限制操作灵活性。我们引入了HYPER - GNC，这是一个多任务强化学习框架，其中超网络将物理信息任务嵌入映射到共享的演员 - 评论家策略的权重，使单个紧凑控制器能够掌握四个不同的GNC任务：速度跟踪、对接、检查和避障导航。连续嵌入空间允许控制器在部署时推广到新的任务配置而无需重新训练。大量实验表明，HYPER - GNC实现了与单任务专家相当的样本效率，同时在显著的惯性扰动和外部力作用下保持稳定。我们还在物理卫星模拟器上验证了该框架，成功弥合了所有任务配置文件的模拟到现实的差距。代码、训练模型和部署脚本已公开提供以支持可重复性。
英文摘要
Autonomous free-flying robots in orbital environments require controllers that are both versatile and resource-efficient, yet maintaining a separate, task-specific policy for each mission profile is architecturally brittle and limits operational flexibility as requirements evolve. We introduce HYPER-GNC, a multi-task reinforcement learning framework in which a hypernetwork maps physics-informed task embeddings to the weights of a shared actor-critic policy, enabling a single compact controller to master four distinct GNC tasks: velocity tracking, docking, inspection, and navigation with obstacle avoidance. The continuous embedding space allows the controller to generalize to novel mission configurations at deployment time without any retraining. Extensive experiments demonstrate that HYPER-GNC achieves sample efficiency comparable to single-task specialists while maintaining stability under significant inertial perturbations and external body wrenches. We further validate the framework on a physical satellite emulator, successfully bridging the simulation-to-reality gap across all mission profiles. Code, trained models, and deployment scripts are made publicly available to support reproducibility.
