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.

翻译：从原始感官数据中表征场景及其构成对象，是使机器人能够与环境交互的核心能力。本文提出一种新颖的场景理解方法，利用分层对象中心生成模型，使智能体能够通过主动推理（一种受神经科学启发的行动与感知框架），在异中心参考系中推断对象类别与姿态。为评估主动视觉智能体的行为，我们同时提出一项新基准测试：在给定特定对象的目标视角条件下，智能体需从随机放置3D对象的工作空间中，寻找最佳匹配视角。实验证明，我们的主动推理智能体能够平衡认知探索与目标驱动行为，并在性能上大幅超越监督学习与强化学习基线方法。