ICML 2025

Hierarchical Planning for Complex Tasks with
Knowledge Graph-RAG and Symbolic Verification
Flavio Petruzzellis 1 Cristina Cornelio 2 Pietro Li`o 3
Abstract
Large Language Models (LLMs) have shown
promise as robotic planners but often struggle
with long-horizon and complex tasks, especially
in specialized environments requiring external
knowledge.
While hierarchical planning and
Retrieval-Augmented Generation (RAG) address
some of these challenges, they remain insufficient
on their own and a deeper integration is required
for achieving more reliable systems. To this end,
we propose a neuro-symbolic approach that en-
hances LLMs-based planners with Knowledge
Graph-based RAG for hierarchical plan genera-
tion. This method decomposes complex tasks
into manageable subtasks, further expanded into
executable atomic action sequences. To ensure
formal correctness and proper decomposition, we
integrate a Symbolic Validator, which also func-
tions as a failure detector by aligning expected
and observed world states. Our evaluation against
baseline methods demonstrates the consistent sig-
nificant advantages of integrating hierarchical
planning, symbolic verification, and RAG across
tasks of varying complexity and different LLMs.
Additionally, our experimental setup and novel
metrics not only validate our approach for com-
plex planning but also serve as a tool for assess-
ing LLMs’ reasoning and compositional capa-
bilities. Code available at https://github.
com/corneliocristina/HVR .
1. Introduction
Humans naturally tackle complex tasks, such as boiling
water, by first conceptualizing high-level steps (e.g., filling
1Department of Mathematics, University of Padova, Padova,
Italy 2Samsung AI, Cambridge, UK 3Computer Science Depart-
ment, University of Cambridge, Cambridge, UK. Correspondence
to: Cristina Cornelio <c.cornelio@samsung.com>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025
by the author(s).
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