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

Gossip Algorithms for Distributed Signal Processing

Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This article presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page

On Building Generalizable Learning Agents

It has been a long-standing goal in Artificial Intelligence (AI) to build machines that can solve tasks that humans can. Thanks to the recent rapid progress in data-driven methods, which train agents to solve tasks by learning from massive training data, there have been many successes in applying such learning approaches to handle and even solve a number of extremely challenging tasks, including image classification, language generation, robotics control, and several multi-player games. The key factor for all these data-driven successes is that the trained agents can generalize to test scenarios that are unseen during training. This generalization capability is the foundation for building any practical AI system. This thesis studies generalization, the fundamental challenge in AI, and proposes solutions to improve the generalization performances of learning agents in a variety of problems. We start by providing a formal formulation of the generalization problem in the context of reinforcement learning and proposing 4 principles within this formulation to guide the design of training techniques for improved generalization. We validate the effectiveness of our proposed principles by considering 4 different domains, from simple to complex, and developing domain-specific techniques following these principles. Particularly, we begin with the simplest domain, i.e., path-finding on graphs (Part I), and then consider visual navigation in a 3D world (Part II) and competition in complex multi-agent games (Part III), and lastly tackle some natural language processing tasks (Part IV). Empirical evidences demonstrate that the proposed principles can generally lead to much improved generalization performances in a wide range of problems

Structures for Sophisticated Behaviour: Feudal Hierarchies and World Models

This thesis explores structured, reward-based behaviour in artificial agents and in animals. In Part I we investigate how reinforcement learning agents can learn to cooperate. Drawing inspiration from the hierarchical organisation of human societies, we propose the framework of Feudal Multi-agent Hierarchies (FMH), in which coordination of many agents is facilitated by a manager agent. We outline the structure of FMH and demonstrate its potential for decentralised learning and control. We show that, given an adequate set of subgoals from which to choose, FMH performs, and particularly scales, substantially better than cooperative approaches that use shared rewards. We next investigate training FMH in simulation to solve a complex information gathering task. Our approach introduces a ‘Centralised Policy Actor-Critic’ (CPAC) and an alteration to the conventional multi-agent policy gradient, which allows one multi-agent system to advise the training of another. We further exploit this idea for communicating agents with shared rewards and demonstrate its efficacy. In Part II we examine how animals discover and exploit underlying statistical structure in their environments, even when such structure is difficult to learn and use. By analysing behavioural data from an extended experiment with rats, we show that such hidden structure can indeed be learned, but also that subjects suffer from imperfections in their ability to infer their current state. We account for their behaviour using a Hidden Markov Model, in which recent observations are integrated imperfectly with evidence from the past. We find that over the course of training, subjects learn to track their progress through the task more accurately, a change that our model largely attributes to the more reliable integration of past evidenc