Anomaly Detection in the Latent Space of VAEs

One of the most important challenges in the development of autonomous driving systems is to make them robust against unexpected or unknown objects. Many of these systems perform really good in a controlled environment where they encounter situation for which they have been trained. In order for them to be safely deployed in the real world, they need to be aware if they encounter situations or novel objects for which the have not been sufficiently trained for in order to prevent possibly dangerous behavior. In reality, they often fail when dealing with such kind of anomalies, and do so without any signs of uncertainty in their predictions. This thesis focuses on the problem of detecting anomalous objects in road images in the latent space of a VAE. For that, normal and anomalous data was used to train the VAE to fit the data onto two prior distributions. This essentially trains the VAE to create an anomaly and a normal cluster. This structure of the latent space makes it possible to detect anomalies in it by using clustering algorithms like k-means. Multiple experiments were carried out in order to improve to separation of normal and anomalous data in the latent space. To test this approach, anomaly data from multiple datasets was used in order to evaluate the detection of anomalies. The approach described in this thesis was able to detect almost all images containing anomalous objects but also suffers from a high false positive rate which still is a common problem of many anomaly detection methods

Prediction of social dynamic agents and long-tailed learning challenges: a survey

Autonomous robots that can perform common tasks like driving, surveillance, and chores have the biggest potential for impact due to frequency of usage, and the biggest potential for risk due to direct interaction with humans. These tasks take place in openended environments where humans socially interact and pursue their goals in complex and diverse ways. To operate in such environments, such systems must predict this behaviour, especially when the behavior is unexpected and potentially dangerous. Therefore, we summarize trends in various types of tasks, modeling methods, datasets, and social interaction modules aimed at predicting the future location of dynamic, socially interactive agents. Furthermore, we describe long-tailed learning techniques from classification and regression problems that can be applied to prediction problems. To our knowledge this is the first work that reviews social interaction modeling within prediction, and long-tailed learning techniques within regression and prediction

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Planetary Science Informatics and Data Analytics Conference : April 24–26, 2018, St. Louis, Missouri

The PSIDA conference provides a forum to discuss approaches, challenges, and applications of informatics and data analytics technologies and capabilities in planetary science.Institutional Support NASA Planetary Data System Geosciences, Lunar and Planetary Institute.Chairs Tom Stein, Washington University, St. Louis, USA, Dan Crichton, Jet Propulsion Laboratory, Pasadena, USA ; Program Committee Alphan Altinok, Jet Propulsion Laboratory, Pasadena, USA … [and 8 others]PARTIAL CONTENTS: ESA Planetary Science Archive Architecture and Data Management--SPICE for ESA Planetary Missions--VESPA: Enlarging the Virtual Observatory to Planetary Science--SeaBIRD: A Flexible and Intuitive Planetary Datamining Infrastructure--Model-Driven Development for PDS4 Software and Services--The Need for a Planetary Spatial Data Clearinghouse--The Relationship Between Planetary Spatial Data Infrastructure and the Planetary Data System--Update on the NASA-USGS Planetary Spatial Data Infrastructure Inter-Agency Agreement--MoonDB - A Data System for Analytical Data of Lunar Samples--Large-Scale Numerical Simulations of Planetary Interiors--Scalable Data Processing with the LROC Processing Pipelines--PACKMAN-Net: A Distributed, Open-Access, and Scalable Network of User-Friendly Space Weather Stations