Thesis title: Learning Symbolic Planning Models from Images
The construction of symbolic planning models, particularly in the PDDL language, remains
a longstanding challenge in Artificial Intelligence due to its reliance on manual domain
engineering.
This thesis explores how such models can be learned automatically and without supervision, from raw visual input. We focus on learning PDDL-style action models for Classical Planning, with an emphasis on planning effectiveness and semantic interpretability.
Our work builds on Latplan, a system that learns symbolic representations from images
via deep latent-space encoding. In the first part of the thesis we improve Latplan without
architectural changes by enforcing logical invariants through fuzzy logic regularization and
tuning hyperparameters. While these methods yield improvements in simpler domains, they
do not overcome Latplan’s two key limitations: the lack of action interpretability and poor
planning performance due to hallucinated plans. To address this, we propose R-Latplan, an
architectural extension introducing deterministic action labeling based on visual differences.
This enhances planning performance and aligns learned actions with high-level behaviors,
even under noise. However, scalability remains a challenge due to the proliferation of generated actions, which increases planning time. To compress the action space, we investigate various techniques, first we use Decision Trees in order to identify patterns among the effects and preconditions of R-Latplan and translate these patterns into higher level actions, but
this approach does not enable efficient action models; then we propose RC-latplan, which
clusters low-level actions into semantically coherent groups, each translated into a PDDL
action. Experiments show that RC-latplan greatly reduces domain size while preserving interpretability. Although it slightly underperforms R-Latplan, a variant, clustering actions with
the same effects achieves logical equivalence with R-Latplan.
Finally, the thesis explores a complementary research in which I worked, on Visual Re-
ward Machines (VRMs), a framework for learning symbolic automata from visual observations to improve learning in sparse-reward environments.