Agentic AI for Robot Teams
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
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 learningsProvides an introduction to LLM-based AI AgentsDescribes an approach to applying LLM-based AI Agents to robotic teamsProvides demonstrations of the approach running in hardware with a heterogeneous team of robotsPresents lessons learned and future work in this areaDownload this free whitepaper now!
