使用主动推理进行以对象为中心的场景表示

IF 2.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Toon Van de Maele;Tim Verbelen;Pietro Mazzaglia;Stefano Ferraro;Bart Dhoedt
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引用次数: 0

摘要

从原始感官数据中呈现场景及其组成物体是机器人与环境互动的核心能力。在这封信中,我们提出了一种新颖的场景理解方法,利用以物体为中心的生成模型,使代理能够使用主动推理(一种用于行动和感知的神经启发框架)在以分配为中心的参考框架中推断出物体的类别和姿势。为了评估主动视觉代理的行为,我们还提出了一个新的基准,即在给定特定物体的目标视角后,代理需要在三维随机定位物体的工作空间中找到最佳匹配视角。我们证明,我们的主动推理代理能够在认识论觅食和目标驱动行为之间取得平衡,并且在成功率方面定量优于监督学习和强化学习基线超过两倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Object-Centric Scene Representations Using Active Inference
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 letter, we propose a novel approach for scene understanding, leveraging an 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 quantitatively outperforms both supervised and reinforcement learning baselines by more than a factor of two in terms of success rate.
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来源期刊
Neural Computation
Neural Computation 工程技术-计算机:人工智能
CiteScore
6.30
自引率
3.40%
发文量
83
审稿时长
3.0 months
期刊介绍: Neural Computation is uniquely positioned at the crossroads between neuroscience and TMCS and welcomes the submission of original papers from all areas of TMCS, including: Advanced experimental design; Analysis of chemical sensor data; Connectomic reconstructions; Analysis of multielectrode and optical recordings; Genetic data for cell identity; Analysis of behavioral data; Multiscale models; Analysis of molecular mechanisms; Neuroinformatics; Analysis of brain imaging data; Neuromorphic engineering; Principles of neural coding, computation, circuit dynamics, and plasticity; Theories of brain function.
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