Search Results for author:
Found 8 papers, 1 papers with code
Planning for Learning Object Properties
Autonomous agents embedded in a physical environment need the ability to recognize objects and their properties from sensory data.
Online Learning of Reusable Abstract Models for Object Goal Navigation
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 Object Goal Navigation task.
On some Foundational Aspects of Human-Centered Artificial Intelligence
The burgeoning of AI has prompted recommendations that AI techniques should be "human-centered".
Online Grounding of Symbolic Planning Domains in Unknown Environments
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 operates.
Deliberative Acting, Online Planning and Learning with Hierarchical Operational Models
However, executing the planned actions has needed operational models, in which rich computational control structures and closed-loop online decision-making are used to specify how to perform an action in a nondeterministic execution context, react to events and adapt to an unfolding situation.
Integrating Acting, Planning and Learning in Hierarchical Operational Models
We present new planning and learning algorithms for RAE, the Refinement Acting Engine.
Incremental Learning of Discrete Planning Domains from Continuous Perceptions
We propose a framework for learning discrete deterministic planning domains.
Incremental learning abstract discrete planning domains and mappings to continuous perceptions
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 is fixed; (ii) the mapping between the observations from the real word and the states is implicitly assumed or learned offline, and it is not part of the planning domain.
