Generating Long-term Trajectories Using Deep Hierarchical Networks

We study the problem of modeling spatiotemporal trajectories over long time horizons using expert demonstrations. For instance, in sports, agents often choose action sequences with long-term goals in mind, such as achieving a certain strategic position. Conventional policy learning approaches, such as those based on Markov decision processes, generally fail at learning cohesive long-term behavior in such high-dimensional state spaces, and are only effective when myopic modeling lead to the desired behavior. The key difficulty is that conventional approaches are "shallow" models that only learn a single state-action policy. We instead propose a hierarchical policy class that automatically reasons about both long-term and short-term goals, which we instantiate as a hierarchical neural network. We showcase our approach in a case study on learning to imitate demonstrated basketball trajectories, and show that it generates significantly more realistic trajectories compared to non-hierarchical baselines as judged by professional sports analysts.Comment: Published in NIPS 201

Audio-Visual Speaker Identification using the CUAVE Database

The freely available nature of the CUAVE database allows it to provide a valuable platform to form benchmarks and compare research. This paper shows that the CUAVE database can successfully be used to test speaker identifications systems, with performance comparable to existing systems implemented on other databases. Additionally, this research shows that the optimal configuration for decisionfusion of an audio-visual speaker identification system relies heavily on the video modality in all but clean speech conditions

Coordinated Multi-Agent Imitation Learning

We study the problem of imitation learning from demonstrations of multiple coordinating agents. One key challenge in this setting is that learning a good model of coordination can be difficult, since coordination is often implicit in the demonstrations and must be inferred as a latent variable. We propose a joint approach that simultaneously learns a latent coordination model along with the individual policies. In particular, our method integrates unsupervised structure learning with conventional imitation learning. We illustrate the power of our approach on a difficult problem of learning multiple policies for fine-grained behavior modeling in team sports, where different players occupy different roles in the coordinated team strategy. We show that having a coordination model to infer the roles of players yields substantially improved imitation loss compared to conventional baselines.Comment: International Conference on Machine Learning 201

A Unified Approach to Multi-Pose Audio-Visual ASR

The vast majority of studies in the field of audio-visual automatic speech recognition (AVASR) assumes frontal images of a speaker's face, but this cannot always be guaranteed in practice. Hence our recent research efforts have concentrated on extracting visual speech information from non-frontal faces, in particular the profile view. The introduction of additional views to an AVASR system increases the complexity of the system, as it has to deal with the different visual features associated with the various views. In this paper, we propose the use of linear regression to find a transformation matrix based on synchronous frontal and profile visual speech data, which is used to normalize the visual speech in each viewpoint into a single uniform view. In our experiments for the task of multi-speaker lipreading, we show that this "pose-invariant" technique reduces train/test mismatch between visual speech features of different views, and is of particular benefit when there is more training data for one viewpoint over another (e.g. frontal over profile)

ASSESSING CHEMICAL WEAPON FACTORS: A CASE STUDY COMPARISON OF ISIS AND AUM SHINRIKYO

This social science research study examines the chemical weapon attributes associated with violent non-state actors (VNSA). The focus is on the question: What factors impact the development and use of chemical weapons by VNSAs? The chemical weapon threat posed by VNSA groups is enduring and predicated on multiple factors, which can determine the effectiveness of such an initiative. By examining these factors and determining which are the most relevant, measures can be taken to counter the threat that chemical weapons pose. This paper attempts to address these concerns by executing a case study comparison of the chemical weapon activities associated with two VNSAs, Aum Shinrikyo and the Islamic State of Iraq and Syria (ISIS), in order to derive insights related to significant differences and similarities between the two organizations. Using the insights from the comparative analysis, recommendations are provided to best address the most critical factors associated with VNSA chemical weapon efforts. Ultimately, this study determined that an increase in available safe haven or an ability to change approaches to technology makes a VNSA chemical weapon effort more likely

Generative Multi-Agent Behavioral Cloning

We propose and study the problem of generative multi-agent behavioral cloning, where the goal is to learn a generative, i.e., non-deterministic, multi-agent policy from pre-collected demonstration data. Building upon advances in deep generative models, we present a hierarchical policy framework that can tractably learn complex mappings from input states to distributions over multi-agent action spaces by introducing a hierarchy with macro-intent variables that encode long-term intent. In addition to synthetic settings, we show how to instantiate our framework to effectively model complex interactions between basketball players and generate realistic multi-agent trajectories of basketball gameplay over long time periods. We validate our approach using both quantitative and qualitative evaluations, including a user study comparison conducted with professional sports analysts