From the 1 of 7 linked papers with an AI index.
7 papers
Interventional Causal Circuits for Safe Robot Action Testing and Failure Recovery
Naren Vasantakumaar, Tom Schierenbeck, Michael Beetz
The paper introduces a closed‑loop system that uses causal diagnosis to pinpoint why a robot action fails safety tests and suggests corrective parameter changes, reducing wasted re…
LLM-Guided Future Hypotheses for Horizon-Aware Exploration in Multi-Step Robot Manipulation
Mohammad Khoshnazar, Andrew Melnik, Michael Beetz
Multi-step robot manipulation requires acting under uncertainty about how the scene will evolve, making exploration and policy adaptation challenging. We study whether short-horizo…
Digital Twin Generation from Visual Data: A Survey
Andrew Melnik, Benjamin Alt, Giang Nguyen +7
This survey examines recent advances in generating digital twins from visual data. These digital twins - virtual 3D replicas of physical assets - can be applied to robotics, media…
Implementing Knowledge Representation and Reasoning with Object Oriented Design
Abdelrhman Bassiouny, Tom Schierenbeck, Sorin Arion +4
This paper introduces KRROOD, a framework designed to bridge the integration gap between modern software engineering and Knowledge Representation & Reasoning (KR&R) systems. While…
Open, Reproducible and Trustworthy Robot-Based Experiments with Virtual Labs and Digital-Twin-Based Execution Tracing
Benjamin Alt, Mareike Picklum, Sorin Arion +2
We envision a future in which autonomous robots conduct scientific experiments in ways that are not only precise and repeatable, but also open, trustworthy, and transparent. To rea…
Grounding Language Models with Semantic Digital Twins for Robotic Planning
Mehreen Naeem, Andrew Melnik, Michael Beetz
We introduce a novel framework that integrates Semantic Digital Twins (SDTs) with Large Language Models (LLMs) to enable adaptive and goal-driven robotic task execution in dynamic…