2 papers
cs.AI2026
On the Generalization Gap in LLM Planning: Tests and Verifier-Reward RL
Valerio Belcamino, Nicholas Attolino, Alessio Capitanelli +1
Recent work shows that fine-tuned Large Language Models (LLMs) can achieve high valid plan rates on PDDL planning tasks. However, it remains unclear whether this reflects transfera…
cs.AI2025
Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM
Nicholas Attolino, Alessio Capitanelli, Fulvio Mastrogiovanni
PDDL-based symbolic task planning remains pivotal for robot autonomy yet struggles with dynamic human-robot collaboration due to scalability, re-planning demands, and delayed plan…