7 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…
Adaptive-Horizon Conflict-Based Search for Closed-Loop Multi-Agent Path Finding
Jiarui Li, Federico Pecora, Runyu Zhang +1
Multi-Agent Path Finding (MAPF) is a core coordination problem for large robot fleets in automated warehouses and logistics. Existing approaches are typically either open-loop plan…
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…
Collaborative Multi-Robot Non-Prehensile Manipulation via Flow-Matching Co-Generation
Yorai Shaoul, Zhe Chen, Mohamed Naveed Gul Mohamed +3
Coordinating a team of robots to reposition multiple objects in cluttered environments requires reasoning jointly about where robots should establish contact, how to manipulate obj…
Symbolic Planning and Multi-Agent Path Finding in Extremely Dense Environments with Unassigned Agents
Bo Fu, Zhe Chen, Rahul Chandan +4
We introduce the Block Rearrangement Problem (BRaP), a challenging component of large warehouse management which involves rearranging storage blocks within dense grids to achieve a…
FICO: Finite-Horizon Closed-Loop Factorization for Unified Multi-Agent Path Finding
Jiarui Li, Alessandro Zanardi, Federico Pecora +2
Multi-Agent Path Finding is a fundamental problem in robotics and AI, yet most existing formulations treat planning and execution separately and address variants of the problem in…