We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial attacks. We formulate a joint probabilistic model that considers a prior distribution over graphs along with a GCN-based likelihood and develop a stochastic variational inference algorithm to estimate the graph posterior and the GCN parameters jointly. To address the problem of propagating gradients through latent variables drawn from discrete distributions, we use their continuous relaxations known as Concrete distributions. We show that, on real datasets, our approach can outperform state-of-the-art Bayesian and non-Bayesian graph neural network algorithms on the task of semi-supervised classification in the absence of graph data and when the network structure is subjected to adversarial perturbations.

In this paper, we present a visually-guided mobile robot that is capable of executing a task requiring complex human-robot interaction (HRI). The robot delivers verbal messages among the inhabitants of an office-like environment. Essential to the robot's robust performance is our behavior-based robot control architecture enhanced with a state of the art decision theoretic planner that takes into account the temporal characteristics of the robot's actions. The decision theoretic layer is based on the partially observable Markov decision process (POMDP) framework allowing us to achieve principled coordination of complex subtasks implemented as robot behaviors/skills. We compute approximate POMDP policies using the randomized point-based value iteration algorithm and we present heuristics for improving its computational efficiency.

We consider the problem of simultaneous localization and mapping (SLAM) using the Rao-Blackwellised particle filter (RBPF) for the class of indoor mobile robots equipped only with stereo vision. Our goal is to construct dense metric maps of natural 3D point landmarks for large cyclic environments in the absence of accurate landmark position measurements and motion estimates. Our work differs from other approaches because landmark estimates are derived from stereo vision and motion estimates are based on sparse optical flow. We distinguish between landmarks using the scale invariant feature transform (SIFT). This is in contrast to current popular approaches that rely on reliable motion models derived from odometric hardware and accurate landmark measurements obtained with laser sensors. Since our approach depends on a particle filter whose main component is the proposal distribution, we develop and evaluate a novel mixture proposal distribution that allows us to robustly close large loops. We validate our approach experimentally for long camera trajectories processing thousands of images at reasonable frame rates

Jose is a visually guided autonomous robotic waiter. He circulates around a room populated by groups of people, politely serving appetizers to humans. The serving task combines elements of robotics with human computer interaction, challenging control architecture with multiple task integration. This paper describes our purely vision-based approach to this task. Methods for mapping, localization and navigation are presented and discussed, including issues of safety for both robots and humans. Our work on human-robot interaction is covered, as well as our solutions to various tasks specific to serving food. We present results of our methods from sample experiments in our laboratory. We further discuss our experiences at the 2001 AAAI mobile robot "Hors D'oeuvres Anyone?" competition, at which Jose took first prize.