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.
IEEE Spectrum — AI files mainly under ai. We currently carry 23 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Bizzabo |
| Canonical URL | https://events.bizzabo.com/867156 |
| Publication time | Mon, 18 May 2026 10:00:01 +0000 |
| Retrieval time | 2026-05-18T10:04:56.157Z |
| Last seen | 2026-05-18T10:04:56.157Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | nSbBJ1sktGbt |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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.
Discussion
0 commentsMore from Bizzabo
