Representing a scene and its constituent objects from raw sensory data is a
core ability for enabling robots to interact with their environment. In this
paper, we propose a novel approach for scene understanding, leveraging a
hierarchical object-centric generative model that enables an agent to infer
object category and pose in an allocentric reference frame using active
inference, a neuro-inspired framework for action and perception. For evaluating
the behavior of an active vision agent, we also propose a new benchmark where,
given a target viewpoint of a particular object, the agent needs to find the
best matching viewpoint given a workspace with randomly positioned objects in
3D. We demonstrate that our active inference agent is able to balance epistemic
foraging and goal-driven behavior, and outperforms both supervised and
reinforcement learning baselines by a large margin.

本文提出了一种基于层次化对象中心生成模型的场景理解方法，利用神经启发式的行动和感知框架实现了代理的对象类别和姿态的推断，并提出了一个新的基准来评估主动视觉代理的行为，并表明我们的主动推理代理在平衡认知觅食和目标驱动行为方面表现优异，远胜于监督和强化学习基线。