2,833 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
An Adversarial Approach to Private Flocking in Mobile Robot Teams
Privacy is an important facet of defence against adversaries. In this letter, we introduce the problem of private flocking . We consider a team of mobile robots flocking in the presence of an adversary, who is able to observe all robots’ trajectories, and who is interested in identifying the leader. We present a method that generates private flocking controllers that hide the identity of the leader robot. Our approach towards privacy leverages a data-driven adversarial co-optimization scheme. We design a mechanism that optimizes flocking control parameters, such that leader inference is hindered. As the flocking performance improves, we successively train an adversarial discriminator that tries to infer the identity of the leader robot. To evaluate the performance of our co-optimization scheme, we investigate different classes of reference trajectories. Although it is reasonable to assume that there is an inherent trade-off between flocking performance and privacy, our results demonstrate that we are able to achieve high flocking performance and simultaneously reduce the risk of revealing the leader
Some New Results in Distributed Tracking and Optimization
The current age of Big Data is built on the foundation of distributed systems, and efficient distributed algorithms to run on these systems.With the rapid increase in the volume of the data being fed into these systems, storing and processing all this data at a central location becomes infeasible. Such a central \textit{server} requires a gigantic amount of computational and storage resources. Even when it is possible to have central servers, it is not always desirable, due to privacy concerns. Also, sending huge amounts of data to such servers incur often infeasible bandwidth requirements.
In this dissertation, we consider two kinds of distributed architectures: 1) star-shaped topology, where multiple worker nodes are connected to, and communicate with a server, but the workers do not communicate with each other; and 2) mesh topology or network of interconnected workers, where each worker can communicate with a small number of neighboring workers.
In the first half of this dissertation (Chapters 2 and 3), we consider distributed systems with mesh topology.We study two different problems in this context. First, we study the problem of simultaneous localization and multi-target tracking. Multiple mobile agents localize themselves cooperatively, while also tracking multiple, unknown number of mobile targets, in the presence of measurement-origin uncertainty. In situations with limited GPS signal availability, agents (like self-driving cars in urban canyons, or autonomous vehicles in hazardous environments) need to rely on inter-agent measurements for localization. The agents perform the additional task of tracking multiple targets (pedestrians and road-signs for self-driving cars). We propose a decentralized algorithm for this problem. To be effective in real-time applications, we propose efficient Gaussian and Gaussian-mixture based filters, rather than the computationally expensive particle-based methods in the existing literature. Our novel factor-graph based approach gives better performance, in terms of both agent localization errors, and target-location and cardinality errors.
Next, we study an online convex optimization problem, where a network of agents cooperate to minimize a global time-varying objective function. Only the local functions are revealed to individual agents. The agents also need to satisfy their individual constraints. We propose a primal-dual update based decentralized algorithm for this problem. Under standard assumptions, we prove that the proposed algorithm achieves sublinear regret and constraint violation across the network. In other words, over a long enough time horizon, the decisions taken by the agents are, on average, as good as if all the information was revealed ahead of time. In addition, the individual constraint violations of the agents, averaged over time, are zero.
In the next part of the dissertation (Chapters 4), we study distributed systems with a star-shaped topology. The problem we study is distributed nonconvex optimization. With the recent success of deep learning, coupled with the use of distributed systems to solve large-scale problems, this problem has gained prominence over the past decade. The recently proposed paradigm of Federated Learning (which has already been deployed by Google/Apple in Android/iOS phones) has further catalyzed research in this direction. The problem we consider is minimizing the average of local smooth, nonconvex functions. Each node has access only to its own loss function, but can communicate with the server, which aggregates updates from all the nodes, before distributing them to all the nodes. With the advent of more and more complex neural network architectures, these updates can be high dimensional. To save resources, the problem needs to be solved via communication-efficient approaches. We propose a novel algorithm, which combines the idea of variance-reduction, with the paradigm of carrying out multiple local updates at each node before averaging. We prove the convergence of the approach to a first-order stationary point. Our algorithm is optimal in terms of computation, and state-of-the-art in terms of the communication requirements.
Lastly in Chapter 5, we consider the situation when the nodes do not have access to function gradients, and need to minimize the loss function using only function values. This problem lies in the domain of zeroth-order optimization. For simplicity of analysis, we study this problem only in the single-node case. This problem finds application in simulation-based optimization, and adversarial example generation for attacking deep neural networks. We propose a novel function value based gradient estimator, which has better variance, and better query-efficiency compared to existing estimators. The proposed estimator covers the most commonly used existing estimators as special cases. We conduct a comprehensive convergence analysis under different conditions. We also demonstrate its effectiveness through a real-world application to generating adversarial examples from a black-box deep neural network
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