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Daniele Meli

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10 papers
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10

AAMAS Conference 2026 Conference Paper

Sample-Efficient Neurosymbolic Deep Reinforcement Learning

  • Celeste Veronese
  • Alessandro Farinelli
  • Daniele Meli

Reinforcement Learning (RL) provides a standard framework for sequential decision-making, but state-of-the-art Deep RL (DRL) methods are often sample-inefficient and struggle to generalize beyond small-scale training scenarios. We propose a neuro-symbolic DRL approach that integrates background symbolic knowledge to improve sample efficiency and generalization to more complex, unseentasks. Partialpolicieslearnedinsimpledomainsaretransferred as logical rules and used for online reasoning to guide learning by biasingexplorationandrescalingQ-valuesduringexploitation. This integration enhances interpretability and accelerates convergence, particularly in sparse-reward and long-horizon settings. Experiments show superior performance over state-of-the-art reward machine methods.

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PRL Workshop 2025 Workshop Paper

Inductive Logic Programming for Heuristic Search

  • Rojina Panta
  • Vedant Khandelwal
  • Celeste Veronese
  • Amit Sheth
  • Daniele Meli
  • Forest Agostinelli

Pathfinding problems are found through computing, chemistry, mathematics, and robotics. Solving pathfinding problems is typically achieved through heuristic search, which is guided by a heuristic function that can be learned using deep neural networks. However, since deep neural networks are typically not explainable, the extraction of new knowledge from these learned heuristic functions is cumbersome. On the other hand, to the best of our knowledge, it has yet to be shown how heuristic functions represented as logic programs, which have been shown to be explainable, can be learned. In this work, we present an algorithm to learn heuristic functions represented as logic programs using dynamic programming and inductive logic programming. Furthermore, we build on dynamic programming concepts to improve the learned logic programs by reusing predicates learned for solving simpler pathfinding problem instances to solve more complex instances. We use the 8-puzzle to demonstrate the effectiveness of our algorithm. Code — https: //github. com/Rojina99/HeurSearchILP

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AAAI Conference 2025 Conference Paper

Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach

  • Daniele Meli
  • Alberto Castellini
  • Alessandro Farinelli

Partially Observable Markov Decision Processes (POMDPs) are a powerful framework for planning under uncertainty. They allow to model state uncertainty as a belief probability distribution. Approximate solvers based on Monte Carlo sampling show great success to relax the computational demand and perform online planning. However, scaling to complex realistic domains with many actions and long planning horizons is still a major challenge, and a key point to achieve good performance is guiding the action-selection process with domain-dependent policy heuristics which are tailored for the specific application domain. We propose to learn high-quality heuristics from POMDP traces of executions generated by any solver. We convert the belief-action pairs to a logical semantics, and exploit data- and time-efficient Inductive Logic Programming (ILP) to generate interpretable belief-based policy specifications, which are then used as online heuristics. We evaluate thoroughly our methodology on two notoriously challenging POMDP problems, involving large action spaces and long planning horizons, namely, rocksample and pocman. Considering different state-of-the-art online POMDP solvers, including POMCP, DESPOT and AdaOPS, we show that learned heuristics expressed in Answer Set Programming (ASP) yield performance superior to neural networks and similar to optimal handcrafted task-specific heuristics within lower computational time. Moreover, they well generalize to more challenging scenarios not experienced in the training phase (e.g., increasing rocks and grid size in rocksample, incrementing the size of the map and the aggressivity of ghosts in pocman).

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NeSy Conference 2025 Conference Paper

Learning Symbolic Persistent Macro-Actions for POMDP Solving Over Time

  • Celeste Veronese
  • Daniele Meli
  • Alessandro Farinelli

This paper proposes an integration of temporal logical reasoning and Partially Observable Markov Decision Processes (POMDPs) to achieve interpretable decision-making under uncertainty with macro-actions. Our method leverages a fragment of Linear Temporal Logic (LTL) based on Event Calculus (EC) to generate persistent (i. e. , constant) macro-actions, which guide Monte Carlo Tree Search (MCTS)-based POMDP solvers over a time horizon, significantly reducing inference time while ensuring robust performance. Such macro-actions are learnt via Inductive Logic Programming (ILP) from a few traces of execution (belief-action pairs), thus eliminating the need for manually designed heuristics and requiring only the specification of the POMDP transition model. In the Pocman and Rocksample benchmark scenarios, our learned macro-actions demonstrate increased expressiveness and generality when compared to time-independent heuristics, indeed offering substantial computational efficiency improvements.

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AAMAS Conference 2025 Conference Paper

Monte Carlo Tree Search with Velocity Obstacles for Safe and Efficient Motion Planning in Dynamic Environments

  • Lorenzo Bonanni
  • Daniele Meli
  • Alberto Castellini
  • Alessandro Farinelli

Online motion planning is a challenging problem for intelligent robots moving in dense environments with dynamic obstacles, e. g. , crowds. In this work, we propose a novel approach for optimal and safe online motion planning with minimal information about dynamic obstacles. Specifically, our approach requires only the current position of the obstacles and their maximum speed, but it does not need any information about their exact trajectories or dynamic model. The proposed methodology combines Monte Carlo Tree Search (MCTS), for online optimal planning via model simulations, with Velocity Obstacles (VO), for obstacle avoidance. We perform experiments in a cluttered simulated environment with walls, and up to 40 dynamic obstacles moving with random velocities and directions. With an ablation study, we show the key contribution of VO in scaling up the efficiency of MCTS, selecting the safest and most rewarding actions in the tree of simulations. Moreover, we show the superiority of our methodology with respect to state-of-the-art planners, including Non-linear Model Predictive Control (NMPC), in terms of improved collision rate, computational and task performance.

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JAIR Journal 2024 Journal Article

Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach

  • Daniele Meli
  • Alberto Castellini
  • Alessandro Farinelli

Partially Observable Markov Decision Processes (POMDPs) are a powerful framework for planning under uncertainty. They allow to model state uncertainty as a belief probability distribution. Approximate solvers based on Monte Carlo sampling show great success to relax the computational demand and perform online planning. However, scaling to complex realistic domains with many actions and long planning horizons is still a major challenge, and a key point to achieve good performance is guiding the action-selection process with domain-dependent policy heuristics which are tailored for the specific application domain. We propose to learn high-quality heuristics from POMDP traces of executions generated by any solver. We convert the belief-action pairs to a logical semantics, and exploit data- and time-efficient Inductive Logic Programming (ILP) to generate interpretable belief-based policy specifications, which are then used as online heuristics. We evaluate thoroughly our methodology on two notoriously challenging POMDP problems, involving large action spaces and long planning horizons, namely, rocksample and pocman. Considering different state-of-the-art online POMDP solvers, including POMCP, DESPOT and AdaOPS, we show that learned heuristics expressed in Answer Set Programming (ASP) yield performance superior to neural networks and similar to optimal handcrafted task-specific heuristics within lower computational time. Moreover, they well generalize to more challenging scenarios not experienced in the training phase (e.g., increasing rocks and grid size in rocksample, incrementing the size of the map and the aggressivity of ghosts in pocman).

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PRL Workshop 2024 Workshop Paper

Towards Neurosymbolic RL via Inductive Learning of Answer Set Programs

  • Celeste Veronese
  • Daniele Meli
  • Alessandro Farinelli

We present a novel approach that combines symbolic, logicbased reasoning with Reinforcement Learning (RL) to improve training outcome without compromising execution time. By integrating Inductive Logic Programming (ILP) with model-free RL, we learn human-interpretable policy heuristics that can bias the exploration process in RL algorithms. Utilizing Answer Set Programming (ASP) for logical representation of policy heuristics, and incremental ILP for efficient data stream management, we demonstrate the effectiveness of our approach on an approximate Q-learner for the Pac-Man scenario. Preliminary results show improved training outcome when offline learned specifications are used to bias the exploration phase. Moreover, the use of ASP heuristics does not significantly impact the training time, and incremental ILP paves the way towards a novel approach to neurosymbolic RL.

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AAMAS Conference 2023 Conference Paper

Learning Logic Specifications for Soft Policy Guidance in POMCP

  • Giulio Mazzi
  • Daniele Meli
  • Alberto Castellini
  • Alessandro Farinelli

Partially Observable Monte Carlo Planning (POMCP) is an efficient solver for Partially Observable Markov Decision Processes (POMDPs). It allows scaling to large state spaces by computing an approximation of the optimal policy locally and online, using a Monte Carlo Tree Search based strategy. However, POMCP suffers from sparse reward function, namely, rewards achieved only when the final goal is reached, particularly in environments with large state spaces and long horizons. Recently, logic specifications have been integrated into POMCP to guide exploration and to satisfy safety requirements. However, such policy-related rules require manual definition by domain experts, especially in real-world scenarios. In this paper, we use inductive logic programming to learn logic specifications from traces of POMCP executions, i. e. , sets of belief-action pairs generated by the planner. Specifically, we learn rules expressed in the paradigm of answer set programming. We then integrate them inside POMCP to provide soft policy bias toward promising actions. In the context of two benchmark scenarios, rocksample and battery, we show that the integration of learned rules from small task instances can improve performance with fewer Monte Carlo simulations and in larger task instances. We make our modified version of POMCP publicly available at https: //github. com/GiuMaz/pomcp_clingo. git.

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ICRA Conference 2022 Conference Paper

Deliberation in autonomous robotic surgery: a framework for handling anatomical uncertainty

  • Eleonora Tagliabue
  • Daniele Meli
  • Diego Dall'Alba
  • Paolo Fiorini

Autonomous robotic surgery requires deliberation, i. e. the ability to plan and execute a task adapting to uncer-tain and dynamic environments. Uncertainty in the surgical domain is mainly related to the partial pre-operative knowledge about patient-specific anatomical properties. In this paper, we introduce a logic-based framework for surgical tasks with deliberative functions of monitoring and learning. The DE-liberative Framework for Robot-Assisted Surgery (DEFRAS) estimates a pre-operative patient-specific plan, and executes it while continuously measuring the applied force obtained from a biomechanical pre-operative model. Monitoring module compares this model with the actual situation reconstructed from sensors. In case of significant mismatch, the learning module is invoked to update the model, thus improving the estimate of the exerted force. DEFRAS is validated both in simulated and real environment with da Vinci Research Kit executing soft tissue retraction. Compared with state-of-the-art related works, the success rate of the task is improved while minimizing the interaction with the tissue to prevent unintentional damage.

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IROS Conference 2020 Conference Paper

Autonomous task planning and situation awareness in robotic surgery

  • Michele Ginesi
  • Daniele Meli
  • Andrea Roberti
  • Nicola Sansonetto
  • Paolo Fiorini

The use of robots in minimally invasive surgery has improved the quality of standard surgical procedures. So far, only the automation of simple surgical actions has been investigated by researchers, while the execution of structured tasks requiring reasoning on the environment and the choice among multiple actions is still managed by human surgeons. In this paper, we propose a framework to implement surgical task automation. The framework consists of a task-level reasoning module based on answer set programming, a low-level motion planning module based on dynamic movement primitives, and a situation awareness module. The logic-based reasoning module generates explainable plans and is able to recover from failure conditions, which are identified and explained by the situation awareness module interfacing to a human supervisor, for enhanced safety. Dynamic Movement Primitives allow to replicate the dexterity of surgeons and to adapt to obstacles and changes in the environment. The framework is validated on different versions of the standard surgical training peg-and-ring task.

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