1 citations · 1 across the 6 of their papers we have counts for
6 papers
Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes
Jonas Ehrhardt, René Heesch, Oliver Niggemann
In this paper, we study Reinforcement Learning in Parametrized Action Markov Decision Processes (PAMDP), where each decision consists of a symbolic action and numerical parameters.…
A Continuous-Time Consistency Model for 3D Point Cloud Generation
Sebastian Eilermann, René Heesch, Oliver Niggemann
Fast and accurate 3D shape generation from point clouds is essential for applications in robotics, AR/VR, and digital content creation. We introduce ConTiCoM-3D, a continuous-time…
Evaluating Large Language Models for Real-World Engineering Tasks
Rene Heesch, Sebastian Eilermann, Alexander Windmann +3
Large Language Models (LLMs) are transformative not only for daily activities but also for engineering tasks. However, current evaluations of LLMs in engineering exhibit two critic…
AAAI Workshop on AI Planning for Cyber-Physical Systems -- CAIPI24
Oliver Niggemann, Gautam Biswas, Alexander Diedrich +3
The workshop 'AI-based Planning for Cyber-Physical Systems', which took place on February 26, 2024, as part of the 38th Annual AAAI Conference on Artificial Intelligence in Vancouv…
On a Uniform Causality Model for Industrial Automation
Maria Krantz, Alexander Windmann, Rene Heesch +2
The increasing complexity of Cyber-Physical Systems (CPS) makes industrial automation challenging. Large amounts of data recorded by sensors need to be processed to adequately perf…
A Research Agenda for AI Planning in the Field of Flexible Production Systems
Aljosha Köcher, Rene Heesch, Niklas Widulle +4
Manufacturing companies face challenges when it comes to quickly adapting their production control to fluctuating demands or changing requirements. Control approaches that encapsul…