Agentic AI for Robot Teams
Recent advancements in agentic AI for collaborative robotic teams were discussed at the Johns Hopkins Applied Physics Laboratory. The presentation outlined challenges related to autonomy, coordination, and adaptability in multi-robot systems, along with a scalable architecture to support these behaviors. Key lessons and future directions for research were also highlighted.
- ▪The presentation focused on agentic AI for collaborative robotic teams.
- ▪It introduced a scalable architecture to enhance autonomy and coordination in heterogeneous systems.
- ▪Demonstrations of the approach were conducted with a team of robots, showcasing practical applications.
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This presentation highlights recent efforts at the Johns Hopkins Applied Physics Laboratory to advance agentic AI for collaborative robotic teams. It begins by framing the core challenges of enabling autonomy, coordination, and adaptability across heterogeneous systems, then introduces a scalable architecture designed to support agentic behaviors in multi-robot environments. The talk concludes with key challenges encountered and practical lessons learned from ongoing research and development. Key learnings Provides an introduction to LLM-based AI Agents Describes an approach to applying LLM-based AI Agents to robotic teams Provides demonstrations of the approach running in hardware with a heterogeneous team of robots Presents lessons learned and future work in this area
Excerpt limited to ~120 words for fair-use compliance. The full article is at Bizzabo.
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