From the 1 of 7 linked papers with an AI index.
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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…
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…
Shadow Program Inversion with Differentiable Planning: A Framework for Unified Robot Program Parameter and Trajectory Optimization
Benjamin Alt, Claudius Kienle, Darko Katic +2
This paper presents SPI-DP, a novel first-order optimizer capable of optimizing robot programs with respect to both high-level task objectives and motion-level constraints. To that…