We Choose to Go to Space: Agent-driven Human and Multi-Robot Collaboration in Microgravity
This addresses collaboration challenges for space exploration, but it appears incremental as it builds on existing multi-agent and simulation methods.
The paper tackles the problem of human and multi-robot collaboration in microgravity by developing SpaceAgents-1, a system that uses a hierarchical architecture with foundation models to enable execution of complex tasks, though no concrete performance numbers are provided.
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.