arXiv subjects

Felix Ocker

Publications and source records attributed to Felix Ocker.

12 recordsLinked to original sources

arXiv2026

Foundation-Model-Based Agents in Industrial Automation: Purposes, Capabilities, and Open Challenges

Foundation models, particularly large language models, are increasingly integrated into agent architectures for industrial tasks such as decision support, process monitoring, and engineering automation. Yet evidence on their purposes, capabilities, and limitations remains fragmented across domains. This work examines how mature foundation-model-based agent systems are in industrial contexts, how their functional profile differs from conventional agent systems, and which limitations persist. A systematic literature survey following the PRISMA 2020 guideline is presented, screening 2,341 publications and synthesising a corpus of 88 publications through a structured coding scheme. The results show that reported systems are predominantly at prototype and early validation stages (75.0% at TRL 4-6), with deployment-oriented evidence remaining rare (9.1%). Operational goals are most frequently positioned in user assistance, monitoring, and process optimisation, while conventional production-control purposes such as planning and scheduling are less prominent. Compared with an established baseline for industrial agent systems, the capability profile reveals substantial gains in human interaction (+37%) and dealing with uncertainty (+35%), but a pronounced deficit in negotiation (-39%). The most widely reported limitations concern lack of generalization, hallucination and output instability, data scarcity, and inference latency. A working definition of foundation-model-based industrial agents is also proposed, bridging conventional agent theory, automation-engineering standards, and the foundation-model paradigm.

cs.AI↗

arXiv2026

MERGE: Guided Vision-Language Models for Multi-Actor Event Reasoning and Grounding in Human-Robot Interaction

We introduce MERGE, a system for situational grounding of actors, objects, and events in dynamic human-robot group interactions. Effective collaboration in such settings requires consistent situational awareness, built on persistent representations of people and objects and an episodic abstraction of events. MERGE achieves this by uniquely identifying physical instances of actors (humans or robots) and objects and structuring them into actor-action-object relations, ensuring temporal consistency across interactions. Central to MERGE is the integration of Vision-Language Models (VLMs) guided with a perception pipeline: a lightweight streaming module continuously processes visual input to detect changes and selectively invokes the VLM only when necessary. This decoupled design preserves the reasoning power and zero-shot generalization of VLMs while improving efficiency, avoiding both the high monetary cost and the latency of frame-by-frame captioning that leads to fragmented and delayed outputs. To address the absence of suitable benchmarks for multi-actor collaboration, we introduce the GROUND dataset, which offers fine-grained situational annotations of multi-person and human-robot interactions. On this dataset, our approach improves the average grounding score by a factor of 2 compared to the performance of VLM-only baselines - including GPT-4o, GPT-5 and Gemini 2.5 Flash - while also reducing run-time by a factor of 4. The code and data are available at www.github.com/HRI-EU/merge.

cs.RO↗

arXiv2025

CARMA: Context-Aware Situational Grounding of Human-Robot Group Interactions by Combining Vision-Language Models with Object and Action Recognition

We introduce CARMA, a system for situational grounding in human-robot group interactions. Effective collaboration in such group settings requires situational awareness based on a consistent representation of present persons and objects coupled with an episodic abstraction of events regarding actors and manipulated objects. This calls for a clear and consistent assignment of instances, ensuring that robots correctly recognize and track actors, objects, and their interactions over time. To achieve this, CARMA uniquely identifies physical instances of such entities in the real world and organizes them into grounded triplets of actors, objects, and actions. To validate our approach, we conducted three experiments, where multiple humans and a robot interact: collaborative pouring, handovers, and sorting. These scenarios allow the assessment of the system's capabilities as to role distinction, multi-actor awareness, and consistent instance identification. Our experiments demonstrate that the system can reliably generate accurate actor-action-object triplets, providing a structured and robust foundation for applications requiring spatiotemporal reasoning and situated decision-making in collaborative settings.

cs.RO↗

arXiv2025

A Grounded Memory System For Smart Personal Assistants

A wide variety of agentic AI applications - ranging from cognitive assistants for dementia patients to robotics - demand a robust memory system grounded in reality. In this paper, we propose such a memory system consisting of three components. First, we combine Vision Language Models for image captioning and entity disambiguation with Large Language Models for consistent information extraction during perception. Second, the extracted information is represented in a memory consisting of a knowledge graph enhanced by vector embeddings to efficiently manage relational information. Third, we combine semantic search and graph query generation for question answering via Retrieval Augmented Generation. We illustrate the system's working and potential using a real-world example.

cs.AI↗

arXiv2025

From Idea to CAD: A Language Model-Driven Multi-Agent System for Collaborative Design

Creating digital models using Computer Aided Design (CAD) is a process that requires in-depth expertise. In industrial product development, this process typically involves entire teams of engineers, spanning requirements engineering, CAD itself, and quality assurance. We present an approach that mirrors this team structure with a Vision Language Model (VLM)-based Multi Agent System, with access to parametric CAD tooling and tool documentation. Combining agents for requirements engineering, CAD engineering, and vision-based quality assurance, a model is generated automatically from sketches and/ or textual descriptions. The resulting model can be refined collaboratively in an iterative validation loop with the user. Our approach has the potential to increase the effectiveness of design processes, both for industry experts and for hobbyists who create models for 3D printing. We demonstrate the potential of the architecture at the example of various design tasks and provide several ablations that show the benefits of the architecture's individual components.

cs.AI↗

arXiv2024

Tulip Agent -- Enabling LLM-Based Agents to Solve Tasks Using Large Tool Libraries

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 tools. In contrast to state-of-the-art implementations, tulip agent does not encode the descriptions of all available tools in the system prompt, which counts against the model's context window, or embed the entire prompt for retrieving suitable tools. Instead, the tulip agent can recursively search for suitable tools in its extensible tool library, implemented exemplarily as a vector store. The tulip agent architecture significantly reduces inference costs, allows using even large tool libraries, and enables the agent to adapt and extend its set of tools. We evaluate the architecture with several ablation studies in a mathematics context and demonstrate its generalizability with an application to robotics. A reference implementation and the benchmark are available at github.com/HRI-EU/tulip_agent.

cs.AI↗

arXiv2024

To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions

How can a robot provide unobtrusive physical support within a group of humans? We present Attentive Support, a novel interaction concept for robots to support a group of humans. It combines scene perception, dialogue acquisition, situation understanding, and behavior generation with the common-sense reasoning capabilities of Large Language Models (LLMs). In addition to following user instructions, Attentive Support is capable of deciding when and how to support the humans, and when to remain silent to not disturb the group. With a diverse set of scenarios, we show and evaluate the robot's attentive behavior, which supports and helps the humans when required, while not disturbing if no help is needed.

cs.RO↗

arXiv2024

LaMI: Large Language Models for Multi-Modal Human-Robot Interaction

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 designs for intent estimation, reasoning, and behavior generation, which were resource-intensive. In contrast, our system empowers researchers and practitioners to regulate robot behavior through three key aspects: providing high-level linguistic guidance, creating "atomic actions" and expressions the robot can use, and offering a set of examples. Implemented on a physical robot, it demonstrates proficiency in adapting to multi-modal inputs and determining the appropriate manner of action to assist humans with its arms, following researchers' defined guidelines. Simultaneously, it coordinates the robot's lid, neck, and ear movements with speech output to produce dynamic, multi-modal expressions. This showcases the system's potential to revolutionize HRI by shifting from conventional, manual state-and-flow design methods to an intuitive, guidance-based, and example-driven approach. Supplementary material can be found at https://hri-eu.github.io/Lami/

cs.RO↗

arXiv2023

Exploring Large Language Models as a Source of Common-Sense Knowledge for Robots

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 ontologies face a challenge because common-sense knowledge is often implicit, i.e., it is obvious to humans but not explicitly stated. This paper investigates if Large Language Models (LLMs) can fill this gap. Our experiments reveal limited effectiveness in the selective extraction of contextual action knowledge, suggesting that LLMs may not be sufficient on their own. However, the large-scale extraction of general, actionable knowledge shows potential, indicating that LLMs can be a suitable tool for efficiently creating ontologies for robots. This paper shows that the technique used for knowledge extraction can be applied to populate a minimalist ontology, showcasing the potential of LLMs in synergy with formal knowledge representation.

cs.RO↗

arXiv2023

CoPAL: Corrective Planning of Robot Actions with Large Language Models

In the pursuit of fully autonomous robotic systems capable of taking over tasks traditionally performed by humans, the complexity of open-world environments poses a considerable challenge. Addressing this imperative, this study contributes to the field of Large Language Models (LLMs) applied to task and motion planning for robots. We propose a system architecture that orchestrates a seamless interplay between multiple cognitive levels, encompassing reasoning, planning, and motion generation. At its core lies a novel replanning strategy that handles physically grounded, logical, and semantic errors in the generated plans. We demonstrate the efficacy of the proposed feedback architecture, particularly its impact on executability, correctness, and time complexity via empirical evaluation in the context of a simulation and two intricate real-world scenarios: blocks world, barman and pizza preparation.

cs.RO↗

arXiv2023

Ontology-Based Feedback to Improve Runtime Control for Multi-Agent Manufacturing Systems

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 of increased OEE, there is a need to develop flexible runtime control strategies for the system. Decentralized strategies, such as multi-agent systems, have proven effective in improving system flexibility. However, runtime multi-agent control of complex manufacturing systems can be challenging as the agents require extensive communication and computational efforts to coordinate agent activities. One way to improve communication speed and cooperation capabilities between system agents is by providing a common language between these agents to represent knowledge about system behavior. The integration of ontology into multi-agent systems in manufacturing provides agents with the capability to continuously update and refine their knowledge in a global context. This paper contributes to the design of an ontology for multi-agent systems in manufacturing, introducing an extendable knowledge base and a methodology for continuously updating the production data by agents during runtime. To demonstrate the effectiveness of the proposed framework, a case study is conducted in a simulated environment, which shows improvements in OEE during runtime.

cs.MA↗

arXiv2022

Maintainability and evolvability of control software in machine and plant manufacturing -- An industrial survey

Automated Production Systems (aPS) have lifetimes of up to 30-50 years, throughout which the desired products change ever more frequently. This requires flexible, reusable control software that can be easily maintained and evolved. To evaluate selected criteria that are especially relevant for maturity in software maintainability and evolvability of aPS, the approach SWMAT4aPS+ builds on a questionnaire with 52 questions. The three main research questions cover updates of software modules and success factors for both cross-disciplinary development as well as reusable models. This paper presents the evaluation results of 68 companies from machine and plant manufacturing (MPM). Companies providing automation devices and/or engineering tools will be able to identify challenges their customers in MPM face. Validity is ensured through feedback of the participating companies and an analysis of the statistical unambiguousness of the results. From a software or systems engineering point of view, almost all criteria are fulfilled below expectations.

cs.SE↗