The REVEAL workshop1 focuses on framing the recommendation problem as a one of making personalized interventions, e.g. deciding to recommend a particular item to a particular user. Moreover, these interventions sometimes depend on each other, where a stream of interactions occurs between the user and the system, and where each decision to recommend something will have an impact on future steps and long-term rewards. This framing creates a number of challenges we will discuss at the workshop. How can recommender systems be evaluated offline in such a context? How can we learn recommendation policies that are aware of these delayed consequences and outcomes?

The REVEAL workshop1 focuses on framing the recommendation problem as a one of making personalized interventions. Moreover, these interventions sometimes depend on each other, where a stream of interactions occurs between the user and the system, and where each decision to recommend something will have an impact on future steps and long-term rewards. This framing creates a number of challenges we will discuss at the workshop. How can recommender systems be evaluated offline in such a context? How can we learn recommendation policies that are aware of these delayed consequences and outcomes?

The inaugural REVEAL workshop1 focuses on revisiting the offline evaluation problem for recommender systems. Being able to perform offline experiments is key to rapid innovation; however practitioners often observe significant differences between offline results and the outcome of an online experiment, where users are actually exposed to the resulting recommendations. This is unfortunate because online experiments take time, can be costly, and require access to a live recommender system, when offline experiments are inherently scalable. How can we bridge that gap between offline and online experiments?

Precise calibration of camera intrinsic and extrinsic parameters, while often useful, is difficult to obtain during field operation and presents scaling issues for multi-robot systems. We demonstrate a vision-based approach to navigation that does not depend on traditional camera calibration, and present an algorithm for guiding a robot through a previously traversed environment using a set of uncalibrated cameras mounted on the robot. On the first excursion through an environment, the system builds a topological representation of the robot's exploration path, encoded as a place graph. On subsequent navigation missions, the method localizes the robot within the graph and provides robust guidance to a specified destination. We combine this method with reactive collision avoidance to obtain a system able to navigate the robot safely and reliably through the environment. We validate our approach with ground-truth experiments and demonstrate the method on a small ground rover navigating through several dynamic environments.

We present a vision-based method that assists human navigation within unfamiliar environments. Our main contribution is a novel algorithm that learns the correlation between user egomotion and feature matches on a wearable set of uncalibrated cameras. The primary advantage of this method is that it provides robust guidance cues in the user's body frame, and is tolerant to small changes in the camera configuration. We couple this method with a topological mapping algorithm that provides global localization within the traversed environment. We validate our approach with ground-truth experiments and demonstrate the method on several real-world datasets spanning two kilometers of indoor and outdoor walking excursions.

Robust egomotion recovery for extended camera excursions has long been a challenge for machine vision researchers. Existing algorithms handle spatially limited environments and tend to consume prohibitive computational resources with increasing excursion time and distance. We describe an egomotion estimation algorithm that takes as input a coarse 3D model of an environment, and an omnidirectional video sequence captured within the environment, and produces as output a reconstruction of the camera's 6-DOF egomotion expressed in the coordinates of the input model. The principal novelty of our method is a robust matching algorithm that associates 2D edges from the video with 3D line segments from the input model. Our system handles 3-DOF and 6-DOF camera excursions of hundreds of meters within real, cluttered environments. It uses a novel prior visibility analysis to speed initialization and dramatically accelerate image-to-model matching. We demonstrate the method's operation, and qualitatively and quantitatively evaluate its performance, on both synthetic and real image sequences.