72 citations · 76 across the 4 of their papers we have counts for
4 papers
Tulip Agent -- Enabling LLM-Based Agents to Solve Tasks Using Large Tool Libraries
Felix Ocker, Daniel Tanneberg, Julian Eggert +1
We introduce tulip agent, an architecture for autonomous LLM-based agents with Create, Read, Update, and Delete access to a tool library containing a potentially large number of to…
LaMI: Large Language Models for Multi-Modal Human-Robot Interaction
Chao Wang, Stephan Hasler, Daniel Tanneberg +5
This paper presents an innovative large language model (LLM)-based robotic system for enhancing multi-modal human-robot interaction (HRI). Traditional HRI systems relied on complex…
Exploring Large Language Models as a Source of Common-Sense Knowledge for Robots
Felix Ocker, Jörg Deigmöller, Julian Eggert
Service robots need common-sense knowledge to help humans in everyday situations as it enables them to understand the context of their actions. However, approaches that use ontolog…
Ontology-Based Feedback to Improve Runtime Control for Multi-Agent Manufacturing Systems
Jonghan Lim, Leander Pfeiffer, Felix Ocker +2
Improving the overall equipment effectiveness (OEE) of machines on the shop floor is crucial to ensure the productivity and efficiency of manufacturing systems. To achieve the goal…