17 citations · 25 across the 9 of their papers we have counts for
9 papers
Acting and Planning with Hierarchical Operational Models on a Mobile Robot: A Study with RAE+UPOM
Oscar Lima, Marc Vinci, Sunandita Patra +6
Robotic task execution faces challenges due to the inconsistency between symbolic planner models and the rich control structures actually running on the robot. In this paper, we pr…
Planning for Learning Object Properties
Leonardo Lamanna, Luciano Serafini, Mohamadreza Faridghasemnia +4
Autonomous agents embedded in a physical environment need the ability to recognize objects and their properties from sensory data. Such a perceptual ability is often implemented by…
Online Learning of Reusable Abstract Models for Object Goal Navigation
Tommaso Campari, Leonardo Lamanna, Paolo Traverso +2
In this paper, we present a novel approach to incrementally learn an Abstract Model of an unknown environment, and show how an agent can reuse the learned model for tackling the Ob…
On some Foundational Aspects of Human-Centered Artificial Intelligence
Luciano Serafini, Raul Barbosa, Jasmin Grosinger +9
The burgeoning of AI has prompted recommendations that AI techniques should be "human-centered". However, there is no clear definition of what is meant by Human Centered Artificial…
Online Grounding of Symbolic Planning Domains in Unknown Environments
Leonardo Lamanna, Luciano Serafini, Alessandro Saetti +2
If a robotic agent wants to exploit symbolic planning techniques to achieve some goal, it must be able to properly ground an abstract planning domain in the environment in which it…
Deliberative Acting, Online Planning and Learning with Hierarchical Operational Models
Sunandita Patra, James Mason, Malik Ghallab +2
In AI research, synthesizing a plan of action has typically used descriptive models of the actions that abstractly specify what might happen as a result of an action, and are tailo…
Integrating Acting, Planning and Learning in Hierarchical Operational Models
Sunandita Patra, James Mason, Amit Kumar +3
We present new planning and learning algorithms for RAE, the Refinement Acting Engine. RAE uses hierarchical operational models to perform tasks in dynamically changing environment…
Incremental Learning of Discrete Planning Domains from Continuous Perceptions
Luciano Serafini, Paolo Traverso
We propose a framework for learning discrete deterministic planning domains. In this framework, an agent learns the domain by observing the action effects through continuous featur…
Incremental learning abstract discrete planning domains and mappings to continuous perceptions
Luciano Serafini, Paolo Traverso
Most of the works on planning and learning, e.g., planning by (model based) reinforcement learning, are based on two main assumptions: (i) the set of states of the planning domain…