Planning and learning to perceive in partially unknown environments

For applying symbolic planning, an agent acting in an environment needs to know its symbolic state, and an abstract model of the environment dynamics. However, in the real world, an agent has low-level perceptions of the environment (e.g. its position given by a GPS sensor), rather than symbolic observations representing its current state. Furthermore, in many real-world scenarios, it is not feasible to provide an agent with a complete and correct model of the environment, e.g., when the environment is (partially) unknown a priori. Therefore, agents need to dynamically learn/adapt/extend their perceptual abilities online, in an autonomous way, by exploring and interacting with the environment where they operate. In this paper, we provide a general architecture of a planning, learning, and acting agent. Moreover, we propose solutions to the problems of automatically training a neural network to recognize object properties, learning the situations where such properties are better perceivable, and planning to get into such situations. We experimentally show the effectiveness of our approach in simulated and complex environments, and we empirically demonstrate the feasibility of our approach in a real-world scenario that involves noisy perceptions and noisy actions on a real robot.