Innovation

The Talk2YourKnowledgeBase system from the fortiss research institute research institute takes natural language input from the user, interpreting it through an interface that connects to a knowledge graph database. When a query is made, the system retrieves relevant context using this interface, ensuring that the response is informed by structured and symbolic data.

To extract precise information, a SPARQL query is generated dynamically, leveraging both a large language model and the knowledge graph database itself. This query is then executed against the database, retrieving the most relevant facts and relationships. Once the data is gathered, the language model processes it, generating a well-formed answer that is coherent, context-aware, and human-readable. If the response contains geometric properties, e.g., regarding specific surfaces of an interaction object or an entity of an environmental description, it may be visualized in a web-based graphical interface, augmenting the generated natural language answer. This combination of communication modalities provides an intuitive and interactive way for the user to engage with the retrieved knowledge.

The entire process runs seamlessly within Docker containers, ensuring consistency, scalability, and ease of deployment across different environments.

The FAME (Future Action Modelling Engine) Lab stands at the cutting edge of robotics research, operating as a virtual research laboratory under the auspices of the ERC project bearing the same name. This ambitious project is dedicated to exploring how robots can conceptualize and deliberate on future actions to preemptively address and avoid execution failures. A central focus of the FAME Lab’s research is enabling robots to learn manipulation tasks by observing instructional videos. This complex process involves the robot identifying essential motion patterns within these videos, understanding the rationale behind their effectiveness without explicit knowledge of the underlying physics, and adapting these critical motions to its own operational context, which introduces a variety of uncertainties. Overcoming these challenges would mark a significant milestone, granting robots the ability to autonomously learn from instructional content, thereby acquiring a wide range of skills and competencies. A practical application of this research could enable a robot to adeptly cut any fruit, using any tool, for any purpose, in any context, showcasing the potential for robots to achieve a remarkable level of autonomous functionality and versatility.

The TraceBot Lab offers a pioneering platform for conducting research with robotic systems uniquely designed to have a profound understanding of their actions and perceptions, specifically targeting the automation of the membrane-based sterility testing process. At the heart of the TraceBot Lab’s mission is the integration of verifiable actions into robotic manipulations, facilitated by advanced reasoning over sensor-actor trails within a comprehensive traceability framework. This framework capitalizes on digital-twin technology, which serves to replicate the physical world within a virtual environment, enhancing robot motion planners with the ability to autonomously execute self-checking procedures. These innovative procedures aim to create a semantic trace of the robot’s actions, ensuring that every manipulation is not only verifiable but also meets the rigorous standards required in regulated environments like healthcare. Through this approach, the TraceBot Lab is setting new benchmarks for the reliability and accountability of robotic systems in critical sectors.

For more information, you can visit the webpage of TraceBot to get a better idea of the complete project.