Disentangling Shape and Pose for Object-Centric Deep Active Inference Models

Abstract

Active inference is a first principles approach for understanding the brain in particular, and sentient agents in general, with the single imperative of minimizing free energy. As such, it provides a computational account for modelling artificial intelligent agents, by defining the agent's generative model and inferring the model parameters, actions and hidden state beliefs. However, the exact specification of the generative model and the hidden state space structure is left to the experimenter, whose design choices influence the resulting behaviour of the agent. Recently, deep learning methods have been proposed to learn a hidden state space structure purely from data, alleviating the experimenter from this tedious design task, but resulting in an entangled, non-interpreteable state space. In this paper, we hypothesize that such a learnt, entangled state space does not necessarily yield the best model in terms of free energy, and that enforcing different factors in the state space can yield a lower model complexity. In particular, we consider the problem of 3D object representation, and focus on different instances of the ShapeNet dataset. We propose a model that factorizes object shape, pose and category, while still learning a representation for each factor using a deep neural network. We show that models, with best disentanglement properties, perform best when adopted by an active agent in reaching preferred observations.

Symmetry and complexity in object-centric deep active inference models

“…In the equivariant representation, the transformation to the input observation is preserved and cascaded into the intermediate space. Equivariant representations have been highly adopted in the context of machine learning [15,[24][25][26], and are considered crucial to achieving generalization features from the model at hand. The adoption of equivariant architectures is favoured in the research of 'disentangled' systems by the machine learning community where they have demonstrated their efficiency in generalization, imagination and abstraction reasoning.…”