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(Visually-Attentive) Uncertainty-Aware Navigation Using Deep Neural Networks

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This wiki will guide you through the installation and running of the package along with documentation of the package.

Method Overview

A family of learning-based methods for (Visually-Attentive) Uncertainty-Aware Navigation is presented in this repo:

Overview of the algorithmic architecture of Attentive ORACLE (A-ORACLE) and ORACLE: We design two deep neural networks to efficiently estimate the uncertainty-aware collision score and the information gains for multiple action sequences, namely the Collision Prediction Network (CPN) and Information gain Prediction Network (IPN), respectively. Both networks assume access to a) either the depth image (CPN) or the stacked matrix of the current depth image and the detection mask (IPN), alongside b) the estimates of the robot’s linear velocities, $z$-axis angular velocity, and roll/pitch angles and c) candidate action sequences in a Motion Primitives Library (MPL). Notably, CPN utilizes $\mathbf{m}_1$ representing the current mean value of $\mathbf{s}t$ and $\mathbf{m}2 ... \mathbf{m}{N\Sigma}$ representing the remaining sigma points of the Unscented Transform to account for the uncertainty in the robot's partial state estimate, while an ensemble of CPNs is used to account for the epistemic uncertainty of the neural network model. The predicted uncertainty-aware collision cost $\hat{c}^{uac}$, information gain $\hat{g}$, and a unit goal vector $\mathbf{n}^g_t$ given by a high-level global planner are used to choose the optimal action sequence to be executed in a receding horizon fashion. When the IPN is not engaged, the method reduces to ORACLE method which ensures safe uncertainty-aware map-less navigation.