17 citations · 18 across the 4 of their papers we have counts for
4 papers
Efficient Test-time Inference for Generative Planning Models with OCL Search
Robert Gieselmann, Mihai Samson, Federico Pecora +1
Generative models have emerged as a powerful paradigm for AI planning, yet their performance remains constrained by the training data distribution. One approach is to improve gener…
Self-Improvement for Fast, High-Quality Plan Generation
Robert Gieselmann, Henrike von Huelsen, Mihai Samson +9
Generative models trained on synthetic plan data are a promising approach to generalized planning. Recent work has focused on finding any valid plan, rather than a high-quality sol…
Fast-dRRT*: Efficient Multi-Robot Motion Planning for Automated Industrial Manufacturing
Andrey Solano, Arne Sieverling, Robert Gieselmann +1
We present Fast-dRRT*, a sampling-based multi-robot planner, for real-time industrial automation scenarios. Fast-dRRT* builds upon the discrete rapidly-exploring random tree (dRRT*…
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning
Julian Bernhard, Robert Gieselmann, Klemens Esterle +1
Interaction-aware planning for autonomous driving requires an exploration of a combinatorial solution space when using conventional search- or optimization-based motion planners. W…