arXiv subjects

Enrico Saccon

Publications and source records attributed to Enrico Saccon.

4 recordsLinked to original sources

arXiv2025

Automated Generation of MDPs Using Logic Programming and LLMs for Robotic Applications

We present a novel framework that integrates Large Language Models (LLMs) with automated planning and formal verification to streamline the creation and use of Markov Decision Processes (MDP). Our system leverages LLMs to extract structured knowledge in the form of a Prolog knowledge base from natural language (NL) descriptions. It then automatically constructs an MDP through reachability analysis, and synthesises optimal policies using the Storm model checker. The resulting policy is exported as a state-action table for execution. We validate the framework in three human-robot interaction scenarios, demonstrating its ability to produce executable policies with minimal manual effort. This work highlights the potential of combining language models with formal methods to enable more accessible and scalable probabilistic planning in robotics.

cs.RO↗

arXiv2025

Combining Large Language Models and Symbolic Reasoning for Multi-Robot Temporal Planning through Explainable Knowledge Bases

We present PLANTOR, a framework for generating and executing multi-robot task plans from natural-language task descriptions through LLM-assisted knowledge-base construction. The approach uses large language models to synthesize a structured Prolog knowledge-base, applies consistency checks to detect and repair modeling errors, generates a high-level symbolic plan, refines it into low-level robot actions, and computes a temporally optimized schedule that is converted into an executable behavior tree. The framework is designed to preserve inspectability by exposing the generated knowledge-base, intermediate plans, and scheduling constraints. We evaluate the approach on scenarios inspired by the Blocks World and Grippers benchmark across multiple language models, and we report both the quality of generated knowledge-bases and the runtime of the planning pipeline. We further demonstrate end-to-end execution in a real multi-arm assembly scenario. The results show that LLM-generated knowledge-bases can substantially reduce manual modeling effort, but may still require human correction. Overall, the paper argues for a hybrid workflow in which language models are used to produce structured symbolic artifacts, while correctness-critical planning and scheduling remain symbolic and inspectable.

cs.AI↗

arXiv2024

Fast Shortest Path Polyline Smoothing With $G^1$ Continuity and Bounded Curvature

In this work, we propose a novel and efficient method for smoothing polylines in motion planning tasks. The algorithm applies to motion planning of vehicles with bounded curvature. In the paper, we show that the generated path: 1) has minimal length, 2) is $G^1$ continuous, and 3) is collision-free by construction, if the hypotheses are respected. We compare our solution with the state-of.the-art and show its convenience both in terms of computation time and of length of the compute path.

cs.RO↗

arXiv2023

When Prolog meets generative models: a new approach for managing knowledge and planning in robotic applications

In this paper, we propose a robot oriented knowledge management system based on the use of the Prolog language. Our framework hinges on a special organisation of knowledge base that enables: 1. its efficient population from natural language texts using semi-automated procedures based on Large Language Models, 2. the bumpless generation of temporal parallel plans for multi-robot systems through a sequence of transformations, 3. the automated translation of the plan into an executable formalism (the behaviour trees). The framework is supported by a set of open source tools and is shown on a realistic application.

cs.RO↗