We present SpaceAgents-1, a system for learning human and multi-robot collaboration (HMRC) strategies under microgravity conditions. Future space exploration requires humans to work together with robots. However, acquiring proficient robot skills and adept collaboration under microgravity conditions poses significant challenges within ground laboratories. To address this issue, we develop a microgravity simulation environment and present three typical configurations of intra-cabin robots. We propose a hierarchical heterogeneous multi-agent collaboration architecture: guided by foundation models, a Decision-Making Agent serves as a task planner for human-robot collaboration, while individual Skill-Expert Agents manage the embodied control of robots. This mechanism empowers the SpaceAgents-1 system to execute a range of intricate long-horizon HMRC tasks.

翻译：我们提出了SpaceAgents-1系统，用于学习微重力条件下的人机与多机器人协作策略。未来的太空探索需要人类与机器人协同工作。然而，在微重力环境下获取熟练的机器人技能和有效的协作能力在地面实验室中面临巨大挑战。为解决这一问题，我们开发了一个微重力仿真环境，并提出了三种典型的舱内机器人配置方案。我们设计了一种层次化异构多智能体协作架构：在基础模型引导下，决策智能体作为人机协作的任务规划器，而各个技能专家智能体负责机器人的具身控制。该机制使SpaceAgents-1系统能够执行一系列复杂的长周期人机与多机器人协作任务。