The world is filled with articulated objects that are difficult to determine
how to use from vision alone, e.g., a door might open inwards or outwards.
Humans handle these objects with strategic trial-and-error: first pushing a
door then pulling if that doesn't work. We enable these capabilities in
autonomous agents by proposing "Hypothesize, Simulate, Act, Update, and Repeat"
(H-SAUR), a probabilistic generative framework that simultaneously generates a
distribution of hypotheses about how objects articulate given input
observations, captures certainty over hypotheses over time, and infer plausible
actions for exploration and goal-conditioned manipulation. We compare our model
with existing work in manipulating objects after a handful of exploration
actions, on the PartNet-Mobility dataset. We further propose a novel
PuzzleBoxes benchmark that contains locked boxes that require multiple steps to
solve. We show that the proposed model significantly outperforms the current
state-of-the-art articulated object manipulation framework, despite using zero
training data. We further improve the test-time efficiency of H-SAUR by
integrating a learned prior from learning-based vision models.

提出了一种名为 H-SAUR 的概率生成框架，能够在没有任何训练数据的情况下实现自动化代理系统的物体操作能力，并比现有工作表现更好，同时进一步提高了 H-SAUR 的测试时间效率。

H-SAUR: 对象关节交互理解中的假设、模拟、行动、更新和重复

H-SAUR: Hypothesize, Simulate, Act, Update, and Repeat for Understanding Object Articulations from Interactions

Most existing fair classifiers rely on sensitive attributes to achieve
fairness. However, for many scenarios, we cannot obtain sensitive attributes
due to privacy and legal issues. The lack of sensitive attributes challenges
many existing works. Though we lack sensitive attributes, for many
applications, there usually exists features or information of various formats
that are relevant to sensitive attributes. For example, a personal purchase
history can reflect his or her race, which would be helpful for learning fair
classifiers on race. However, the work on exploring relevant features for
learning fair models without sensitive attributes is rather limited. Therefore,
in this paper, we study a novel problem of learning fair models without
sensitive attributes by exploring relevant features. We propose a probabilistic
generative framework to effectively estimate the sensitive attribute from the
training data with relevant features in various formats and utilize the
estimated sensitive attribute information to learn fair models. Experimental
results on real-world datasets show the effectiveness of our framework in terms
of both accuracy and fairness.

本文提出了一种基于概率生成模型的方法，通过在采用了多种形式的相关特征的训练数据中有效地估计敏感属性信息，从而提高公平模型的学习率。实际测试表明了该方法的准确性和公平性。