11 citations · 12 across the 8 of their papers we have counts for
9 papers
Focusing Robot Open-Ended Reinforcement Learning Through Users' Purposes
Emilio Cartoni, Gianluca Cioccolini, Gianluca Baldassarre
Open-Ended Learning (OEL) autonomous robots can acquire new skills and knowledge through direct interaction with their environment, relying on mechanisms such as intrinsic motivati…
The Autonomy-Alignment Problem in Open-Ended Learning Robots: Formalising the Purpose Framework
Gianluca Baldassarre, Richard J. Duro, Emilio Cartoni +3
The rapid advancement of artificial intelligence is enabling the development of increasingly autonomous robots capable of operating beyond engineered factory settings and into the…
A method for the ethical analysis of brain-inspired AI
Michele Farisco, Gianluca Baldassarre, Emilio Cartoni +6
Despite its successes, to date Artificial Intelligence (AI) is still characterized by a number of shortcomings with regards to different application domains and goals. These limita…
A Computational Model of Representation Learning in the Brain Cortex, Integrating Unsupervised and Reinforcement Learning
Giovanni Granato, Emilio Cartoni, Federico Da Rold +2
A common view on the brain learning processes proposes that the three classic learning paradigms -- unsupervised, reinforcement, and supervised -- take place in respectively the co…
REAL-X -- Robot open-Ended Autonomous Learning Architectures: Achieving Truly End-to-End Sensorimotor Autonomous Learning Systems
Emilio Cartoni, Davide Montella, Jochen Triesch +1
Open-ended learning is a core research field of developmental robotics and AI aiming to build learning machines and robots that can autonomously acquire knowledge and skills increm…
Learning High-Level Planning Symbols from Intrinsically Motivated Experience
Angelo Oddi, Riccardo Rasconi, Emilio Cartoni +3
In symbolic planning systems, the knowledge on the domain is commonly provided by an expert. Recently, an automatic abstraction procedure has been proposed in the literature to cre…
Autonomous Reinforcement Learning of Multiple Interrelated Tasks
Vieri Giuliano Santucci, Gianluca Baldassarre, Emilio Cartoni
Autonomous multiple tasks learning is a fundamental capability to develop versatile artificial agents that can act in complex environments. In real-world scenarios, tasks may be in…
Autonomous Open-Ended Learning of Interdependent Tasks
Vieri Giuliano Santucci, Emilio Cartoni, Bruno Castro da Silva +1
Autonomy is fundamental for artificial agents acting in complex real-world scenarios. The acquisition of many different skills is pivotal to foster versatile autonomous behaviour a…
Autonomous discovery of the goal space to learn a parameterized skill
Emilio Cartoni, Gianluca Baldassarre
A parameterized skill is a mapping from multiple goals/task parameters to the policy parameters to accomplish them. Existing works in the literature show how a parameterized skill…