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If a robotic agent wants to exploit symbolic planning techniques to achievesome goal, it must be able to properly ground an abstract planning domain inthe environment in which it operates. However, if the environment is initiallyunknown by the agent, the agent needs to explore it and discover the salientaspects of the environment needed to reach its goals. Namely, the agent has todiscover: (i) the objects present in the environment, (ii) the properties ofthese objects and their relations, and finally (iii) how abstract actions canbe successfully executed. The paper proposes a framework that aims toaccomplish the aforementioned perspective for an agent that perceives theenvironment partially and subjectively, through real value sensors (e.g., GPS,and on-board camera) and can operate in the environment through low levelactuators (e.g., move forward of 20 cm). We evaluate the proposed architecturein photo-realistic simulated environments, where the sensors are RGB-D on-boardcamera, GPS and compass, and low level actions include movements,grasping/releasing objects, and manipulating objects. The agent is placed in anunknown environment and asked to find objects of a certain type, place anobject on top of another, close or open an object of a certain type. We compareour approach with the state of the art methods on object goal navigation basedon reinforcement learning, showing better performances.

Online Grounding of Symbolic Planning Domains in Unknown Environments