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

High-Dimensional Motion Planning and Learning Under Uncertain Conditions

Many existing path planning methods do not adequately account for uncertainty. Without uncertainty these existing techniques work well, but in real world environments they struggle due to inaccurate sensor models, arbitrarily moving obstacles, and uncertain action consequences. For example, picking up and storing childrens toys is a simple task for humans. Yet, for a robotic household robot the task can be daunting. The room must be modeled with sensors, which may or may not detect all the strewn toys. The robot must be able to detect and avoid the child who may be moving the very toys that the robot is tasked with cleaning. Finally, if the robot missteps and places a foot on a toy, it must be able to compensate for the unexpected consequences of its actions. This example demonstrates that even simple human tasks are fraught with uncertainties that must be accounted for in robotic path planning algorithms. This work presents the first steps towards migrating sampling-based path planning methods to real world environments by addressing three different types of uncertainty: (1) model uncertainty, (2) spatio-temporal obstacle uncertainty (moving obstacles) and (3) action consequence uncertainty. Uncertainty is encoded directly into path planning through a data structure in order to successfully and efficiently identify safe robot paths in sensed environments with noise. This encoding produces comparable clearance paths to other planning methods which are a known for high clearance, but at an order of magnitude less computational cost. It also shows that formal control theory methods combined with path planning provides a technique that has a 95% collision-free navigation rate with 300 moving obstacles. Finally, it demonstrates that reinforcement learning can be combined with planning data structures to autonomously learn motion controls of a seven degree of freedom robot despite a low computational cost despite the number of dimensions

Exploiting Opponent Modeling For Learning In Multi-agent Adversarial Games

An issue with learning effective policies in multi-agent adversarial games is that the size of the search space can be prohibitively large when the actions of both teammates and opponents are considered simultaneously. Opponent modeling, predicting an opponent’s actions in advance of execution, is one approach for selecting actions in adversarial settings, but it is often performed in an ad hoc way. In this dissertation, we introduce several methods for using opponent modeling, in the form of predictions about the players’ physical movements, to learn team policies. To explore the problem of decision-making in multi-agent adversarial scenarios, we use our approach for both offline play generation and real-time team response in the Rush 2008 American football simulator. Simultaneously predicting the movement trajectories, future reward, and play strategies of multiple players in real-time is a daunting task but we illustrate how it is possible to divide and conquer this problem with an assortment of data-driven models. By leveraging spatio-temporal traces of player movements, we learn discriminative models of defensive play for opponent modeling. With the reward information from previous play matchups, we use a modified version of UCT (Upper Conference Bounds applied to Trees) to create new offensive plays and to learn play repairs to counter predicted opponent actions. iii In team games, players must coordinate effectively to accomplish tasks while foiling their opponents either in a preplanned or emergent manner. An effective team policy must generate the necessary coordination, yet considering all possibilities for creating coordinating subgroups is computationally infeasible. Automatically identifying and preserving the coordination between key subgroups of teammates can make search more productive by pruning policies that disrupt these relationships. We demonstrate that combining opponent modeling with automatic subgroup identification can be used to create team policies with a higher average yardage than either the baseline game or domain-specific heuristics

Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots.

The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly referred to as a gas distribution map, to subsequently take actions that depend on the collected information. Since the majority of gas transducers require physical contact with the analyte to sense it, the generation of such a map usually involves slow and laborious data collection from all key locations. In this regard, this paper proposes an efficient exploration algorithm for 2D gas distribution mapping with an autonomous mobile robot. Our proposal combines a Gaussian Markov random field estimator based on gas and wind flow measurements, devised for very sparse sample sizes and indoor environments, with a partially observable Markov decision process to close the robot’s control loop. The advantage of this approach is that the gas map is not only continuously updated, but can also be leveraged to choose the next location based on how much information it provides. The exploration consequently adapts to how the gas is distributed during run time, leading to an efficient sampling path and, in turn, a complete gas map with a relatively low number of measurements. Furthermore, it also accounts for wind currents in the environment, which improves the reliability of the final gas map even in the presence of obstacles or when the gas distribution diverges from an ideal gas plume. Finally, we report various simulation experiments to evaluate our proposal against a computer-generated fluid dynamics ground truth, as well as physical experiments in a wind tunnel.Partial funding for open access charge: Universidad de Málag

Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation

Deep reinforcement learning (DRL) provides a promising way for intelligent agents (e.g., autonomous vehicles) to learn to navigate complex scenarios. However, DRL with neural networks as function approximators is typically considered a black box with little explainability and often suffers from suboptimal performance, especially for autonomous navigation in highly interactive multi-agent environments. To address these issues, we propose three auxiliary tasks with spatio-temporal relational reasoning and integrate them into the standard DRL framework, which improves the decision making performance and provides explainable intermediate indicators. We propose to explicitly infer the internal states (i.e., traits and intentions) of surrounding agents (e.g., human drivers) as well as to predict their future trajectories in the situations with and without the ego agent through counterfactual reasoning. These auxiliary tasks provide additional supervision signals to infer the behavior patterns of other interactive agents. Multiple variants of framework integration strategies are compared. We also employ a spatio-temporal graph neural network to encode relations between dynamic entities, which enhances both internal state inference and decision making of the ego agent. Moreover, we propose an interactivity estimation mechanism based on the difference between predicted trajectories in these two situations, which indicates the degree of influence of the ego agent on other agents. To validate the proposed method, we design an intersection driving simulator based on the Intelligent Intersection Driver Model (IIDM) that simulates vehicles and pedestrians. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics and provides explainable intermediate indicators (i.e., internal states, and interactivity scores) for decision making.Comment: 18 pages, 14 figure

Adaptive Sampling For Efficient Online Modelling

This thesis examines methods enabling autonomous systems to make active sampling and planning decisions in real time. Gaussian Process (GP) regression is chosen as a framework for its non-parametric approach allowing flexibility in unknown environments. The first part of the thesis focuses on depth constrained full coverage bathymetric surveys in unknown environments. Algorithms are developed to find and follow a depth contour, modelled with a GP, and produce a depth constrained boundary. An extension to the Boustrophedon Cellular Decomposition, Discrete Monotone Polygonal Partitioning is developed allowing efficient planning for coverage within this boundary. Efficient computational methods such as incremental Cholesky updates are implemented to allow online Hyper Parameter optimisation and fitting of the GP's. This is demonstrated in simulation and the field on a platform built for the purpose. The second part of this thesis focuses on modelling the surface salinity profiles of estuarine tidal fronts. The standard GP model assumes evenly distributed noise, which does not always hold. This can be handled with Heteroscedastic noise. An efficient new method, Parametric Heteroscedastic Gaussian Process regression, is proposed. This is applied to active sample selection on stationary fronts and adaptive planning on moving fronts where a number of information theoretic methods are compared. The use of a mean function is shown to increase the accuracy of predictions whilst reducing optimisation time. These algorithms are validated in simulation. Algorithmic development is focused on efficient methods allowing deployment on platforms with constrained computational resources. Whilst the application of this thesis is Autonomous Surface Vessels, it is hoped the issues discussed and solutions provided have relevance to other applications in robotics and wider fields such as spatial statistics and machine learning in general

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Data-driven decision making is becoming an integral part of manufacturing companies. Data is collected and commonly used to improve efficiency and produce high quality items for the customers. IoT-based and other forms of object tracking are an emerging tool for collecting movement data of objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over space and time. Movement data can provide valuable insights like process bottlenecks, resource utilization, effective working time etc. that can be used for decision making and improving efficiency. Turning movement data into valuable information for industrial management and decision making requires analysis methods. We refer to this process as movement analytics. The purpose of this document is to review the current state of work for movement analytics both in manufacturing and more broadly. We survey relevant work from both a theoretical perspective and an application perspective. From the theoretical perspective, we put an emphasis on useful methods from two research areas: machine learning, and logic-based knowledge representation. We also review their combinations in view of movement analytics, and we discuss promising areas for future development and application. Furthermore, we touch on constraint optimization. From an application perspective, we review applications of these methods to movement analytics in a general sense and across various industries. We also describe currently available commercial off-the-shelf products for tracking in manufacturing, and we overview main concepts of digital twins and their applications

Environment and task modeling of long-term-autonomous service robots

Utilizing service robots in real-world tasks can significantly improve efficiency, productivity, and safety in various fields such as healthcare, hospitality, and transportation. However, integrating these robots into complex, human-populated environments for continuous use is a significant challenge. A key potential for addressing this challenge lies in long-term modeling capabilities to navigate, understand, and proactively exploit these environments for increased safety and better task performance. For example, robots may use this long-term knowledge of human activity to avoid crowded spaces when navigating or improve their human-centric services. This thesis proposes comprehensive approaches to improve the mapping, localization, and task fulfillment capabilities of service robots by leveraging multi-modal sensor information and (long- term) environment modeling. Learned environmental dynamics are actively exploited to improve the task performance of service robots. As a first contribution, a new long-term-autonomous service robot is presented, designed for both inside and outside buildings. The multi-modal sensor information provided by the robot forms the basis for subsequent methods to model human-centric environments and human activity. It is shown that utilizing multi-modal data for localization and mapping improves long-term robustness and map quality. This especially applies to environments of varying types, i.e., mixed indoor and outdoor or small-scale and large-scale areas. Another essential contribution is a regression model for spatio-temporal prediction of human activity. The model is based on long-term observations of humans by a mobile robot. It is demonstrated that the proposed model can effectively represent the distribution of detected people resulting from moving robots and enables proactive navigation planning. Such model predictions are then used to adapt the robot’s behavior by synthesizing a modular task control model. A reactive executive system based on behavior trees is introduced, which actively triggers recovery behaviors in the event of faults to improve the long-term autonomy. By explicitly addressing failures of robot software components and more advanced problems, it is shown that errors can be solved and potential human helpers can be found efficiently