Environmental sustainability is a worldwide key challenge attracting increasing attention due
to climate change, pollution, and biodiversity decline. Reinforcement learning, initially …

Abstract Partially Observable Markov Decision Processes (POMDPs) are a powerful
framework for planning under uncertainty. They allow to model state uncertainty as a belief …

This paper presents a novel approach combining inductive logic programming with
reinforcement learning to improve training performance and explainability. We exploit …

In this work, we focus on safe policy improvement in multi-agent domains where current
state-of-the-art methods cannot be effectively applied because of large state and action …

We propose a solution for Active Visual Search of objects in an environment, whose 2D floor
map is the only known information. Our solution has three key features that make it more …

Partially observable Markov decision processes (POMDPs) have been widely used in many
robotic applications for sequential decision-making under uncertainty. POMDP online …

We propose a model-based reinforcement learning method using Monte Carlo Tree Search
planning. The approach assumes a black-box approximated model of the environment …

The increasing level of autonomy of robots poses challenges of trust and social acceptance,
especially in human-robot interaction scenarios. This requires an interpretable …

Motion planning in dynamic environments is a challenging robotic task, requiring collision
avoidance and real-time computation. State-of-the-art online methods as Velocity Obstacles …

Online motion planning is a challenging problem for intelligent robots moving in dense
environments with dynamic obstacles, eg, crowds. In this work, we propose a novel …