Unsupervised Grounding of Plannable First-Order Logic Representation from Images

Understand

Recently, there is an increasing interest in obtaining the relational structures of the environment in the Reinforcement Learning community.

• However, the resulting "relations" are not the discrete, logical predicates compatible to the symbolic reasoning such as classical planning or goal recognition.
• Meanwhile, Latplan (Asai and Fukunaga 2018) bridged the gap between deep-learning perceptual systems and symbolic classical planners.
• One key component of the system is a Neural Network called State AutoEncoder (SAE), which encodes an image-based input into a propositional representation compatible to classical planning.