In order to trust the predictions of a machine learning algorithm, it is
necessary to understand the factors that contribute to those predictions. In
the case of probabilistic and uncertainty-aware models, it is necessary to
understand not only the reasons for the predictions themselves, but also the
model's level of confidence in those predictions. In this paper, we show how
existing methods in explainability can be extended to uncertainty-aware models
and how such extensions can be used to understand the sources of uncertainty in
a model's predictive distribution. In particular, by adapting permutation
feature importance, partial dependence plots, and individual conditional
expectation plots, we demonstrate that novel insights into model behaviour may
be obtained and that these methods can be used to measure the impact of
features on both the entropy of the predictive distribution and the
log-likelihood of the ground truth labels under that distribution. With
experiments using both synthetic and real-world data, we demonstrate the
utility of these approaches in understanding both the sources of uncertainty
and their impact on model performance.

这篇论文介绍了如何将解释性方法扩展到具有不确定性意识的模型中，并演示了如何通过这些方法深入理解模型行为、特征对预测分布熵和真实标签对数似然的影响，以及不确定性来源对模型性能的影响。

预测不确定性的模型无关变量重要性：一种基于熵的方法

Model-agnostic variable importance for predictive uncertainty: an  entropy-based approach

Uncertainty in control and perception poses challenges for autonomous vehicle
navigation in unstructured environments, leading to navigation failures and
potential vehicle damage. This paper introduces a framework that minimizes
control and perception uncertainty to ensure safe and reliable navigation. The
framework consists of two uncertainty-aware models: a learning-based vehicle
dynamics model and a self-supervised traversability estimation model. We train
a vehicle dynamics model that can quantify the epistemic uncertainty of the
model to perform active exploration, resulting in the efficient collection of
training data and effective avoidance of uncertain state-action spaces. In
addition, we employ meta-learning to train a traversability cost prediction
network. The model can be trained with driving data from a variety of types of
terrain, and it can online-adapt based on interaction experiences to reduce the
aleatoric uncertainty. Integrating the dynamics model and traversability cost
prediction model with a sampling-based model predictive controller allows for
optimizing trajectories that avoid uncertain terrains and state-action spaces.
Experimental results demonstrate that the proposed method reduces uncertainty
in prediction and improves stability in autonomous vehicle navigation in
unstructured environments.

该研究介绍了一种最小化控制和感知不确定性以确保安全可靠的自主车辆导航的框架，由两个不确定性感知模型组成：基于机器学习的车辆动力学模型和自我监督遍历估计模型。

通过控制和感知的不确定性最小化，在非结构化环境中进行安全导航

Safe Navigation in Unstructured Environments by Minimizing Uncertainty  in Control and Perception

Accurate rotation estimation is at the heart of robot perception tasks such
as visual odometry and object pose estimation. Deep neural networks have
provided a new way to perform these tasks, and the choice of rotation
representation is an important part of network design. In this work, we present
a novel symmetric matrix representation of the 3D rotation group, SO(3), with
two important properties that make it particularly suitable for learned models:
(1) it satisfies a smoothness property that improves convergence and
generalization when regressing large rotation targets, and (2) it encodes a
symmetric Bingham belief over the space of unit quaternions, permitting the
training of uncertainty-aware models. We empirically validate the benefits of
our formulation by training deep neural rotation regressors on two data
modalities. First, we use synthetic point-cloud data to show that our
representation leads to superior predictive accuracy over existing
representations for arbitrary rotation targets. Second, we use image data
collected onboard ground and aerial vehicles to demonstrate that our
representation is amenable to an effective out-of-distribution (OOD) rejection
technique that significantly improves the robustness of rotation estimates to
unseen environmental effects and corrupted input images, without requiring the
use of an explicit likelihood loss, stochastic sampling, or an auxiliary
classifier. This capability is key for safety-critical applications where
detecting novel inputs can prevent catastrophic failure of learned models.

本文介绍了一种用于学习模型的 Symmetric Matrix 表征方法，它满足平滑性和对单元四元数的置信度建模，可用于机器人感知领域中的精确旋转估计，包括 VO 和对象姿态估计。作者针对点云数据和图像数据进行了实证验证，并证明该方法可有效提高对未知环境及图像的鲁棒性。