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Found 3 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.
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.
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