Dec 18, 2021 · The paper proposes a framework that aims to accomplish the aforementioned perspective for an agent that perceives the environment partially and subjectively.

The agent is placed in an unknown environment and asked to find objects of a certain type, place an object on top of another, close or open an object of a ...

Planning and learning to perceive in partially unknown environments · A Framework to Co-Optimize Robot Exploration and Task Planning in Unknown Environments.

In this scheme, the agent explores the unknown environment until discovering all the objects needed to satisfy the goal and then invokes a symbolic planner to ...

For this purpose, we integrate machine learning models to abstract the sensory data, symbolic planning for goal achievement and path planning for navigation. We ...

Dec 18, 2021 · To effectively use an abstract (PDDL) planning domain to achieve goals in an unknown environment, an agent must instantiate such a domain ...

Online grounding of symbolic planning domains in unknown environments. L Lamanna, L Serafini, A Saetti, A Gerevini, P Traverso. Proceedings of the International ...

This paper proposes an algorithm inspired by both plan- ning and Reinforcement Learning to automatically extract a symbolic planning domain for complex dynamic ...

Aug 5, 2022 · Special Session on KR and Robotics. Online Grounding of Symbolic Planning Domains in Unknown Environments. Leonardo Lamanna; Luciano Serafini ...

His Ph.D. topic was the integration of data-driven learning and symbolic planning techniques for agents acting in unknown environments. On November 2023 he won ...