Affordances are fundamental descriptors of relationships between actions, objects and effects. They provide the means whereby a robot can predict effects, recognize actions, select objects and plan its behavior according to desired goals. This paper approaches the problem of an embodied agent exploring the world and learning these affordances autonomously from its sensory experiences. Models exist for learning the structure and the parameters of a Bayesian Network encoding this knowledge. Although Bayesian Networks are capable of dealing with uncertainty and redundancy, previous work considered complete observability of the discrete sensory data, which may lead to hard errors in the presence of noise. In this paper we consider a probabilistic representation of the sensors by Gaussian Mixture Models (GMMs) and explicitly taking into account the probability distribution contained in each discrete affordance concept, which can lead to a more correct learning.

翻译：可负担性是描述动作、对象与效果之间关系的基本表征，它为机器人预测效果、识别动作、选择对象以及根据期望目标规划行为提供了实现手段。本文探讨具身代理在探索世界过程中，从感官经验自主习得这些可负担性的问题。现有模型能够通过学习贝叶斯网络的结构和参数来编码此类知识。尽管贝叶斯网络能够处理不确定性与冗余信息，但以往研究假设离散感官数据具有完全可观测性，这可能在噪声环境下导致硬性错误。本文提出采用高斯混合模型对传感器进行概率表征，并显式考虑每个离散可负担概念中包含的概率分布，从而实现更准确的学习过程。