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

Learning and leveraging kinematics for robot motion planning under uncertainty

Service robots that can assist humans in performing day-to-day tasks will need to be general-purpose robots that can perform a wide array of tasks without much supervision from end-users. As they will be operating in unstructured and ever-changing human environments, they will need to be capable of adapting to their work environments quickly and learning to perform novel tasks within a few trials. However, current robots fall short of these requirements as they are generally highly specialized, can only perform fixed, predefined tasks reliably, and need to operate in controlled environments. One of the main reasons behind this big gap is that the current robots require complete and accurate information about their surroundings to function effectively, whereas, in human environments, robots will only have access to limited information about their tasks and environments. With incomplete information about its surroundings, a robot using pre-programmed or pre-learned motion policies will fail to adapt to the novel situations encountered during operation and fall short in completing its tasks. Online motion generation methods that do not reason about the lack of information will not suffice either, as the developed policies may be unreliable under incomplete information. Reasoning about the lack of information becomes critical for manipulation tasks a service robot would have to perform. These tasks will often require interacting with multiple objects that make or break contacts during the task. A contact between objects can significantly alter their subsequent motion and lead to sudden transitions in their dynamics. Under these sudden transitions, even minor errors in estimating object poses can cause drastic deviations from the robot's initial motion plan for the task and lead the robot to failure in completing the tasks. Hence, service robots need methods that generate motion policies for manipulation tasks efficiently while accounting for the uncertainty due to incomplete or partial information. Partially Observable Markov Decision Processes (POMDPs) is one such mathematical framework that can model and plan for tasks where the agent lacks complete information about the task. However, POMDPs incur exponentially increasing computational costs with planning time horizon, which restricts the current POMDP-based planning methods to problems having short time horizons. Another challenge for planning-based approaches is that they require a state transition function for the world they are operating in to develop motion plans, which may not always be available to the robot. In control theory terms, a state transition function for the world is analogous to its system plant. In this dissertation, we propose to address these challenges by developing methods that can learn state transition functions for robot manipulation tasks directly from observations and later use them to generate long-horizon motion plans to complete the task under uncertainty. We first model the world state transition functions for robot manipulation tasks involving sudden transitions, such as due to contacts, using hybrid models and develop a novel hierarchical POMDP-planner that leverages the representational power of hybrid models to develop motion plans for long-horizon tasks under uncertainty. Next, we address the requirement of planning-based methods to have access to world state transition functions. We introduce three novel methods for learning kinematic models for articulated objects directly from observations and present an algorithm to construct the state transition functions from the learned kinematics models for manipulating these objects. We focus on learning models for articulated objects as they form one of the biggest sets of household objects that service robots will frequently interact with. The first method, MICAH, focuses on learning kinematic models for articulated objects that exhibit configuration-dependent articulation properties, such as a refrigerator door that stays closed magnetically, from unsegmented sequences of observations of object part poses. Next, we introduce ScrewNet, which removes the requirement of object pose estimation of MICAH and learns articulation properties of objects directly from raw sensory data available to the robot (depth images) without knowing their articulation model category a priori. Extending it further, we introduce DUST-net, which learns distributions over articulation model parameters for objects indicating the network's confidence over the estimated parameters directly from raw depth images. Combining these methods, in this dissertation, we introduce a unified framework that can enable a robot to learn state transition functions for manipulation tasks from observations and later use them to develop long-horizon plans even under uncertainty.Mechanical Engineerin

Analysis of Heterogeneous Data Sources for Veterinary Syndromic Surveillance to Improve Public Health Response and Aid Decision Making

The standard technique of implementing veterinary syndromic surveillance (VSyS) is the detection of temporal or spatial anomalies in the occurrence of health incidents above a set threshold in an observed population using the Frequentist modelling approach. Most implementation of this technique also requires the removal of historical outbreaks from the datasets to construct baselines. Unfortunately, some challenges exist, such as data scarcity, delayed reporting of health incidents, and variable data availability from sources, which make the VSyS implementation and alarm interpretation difficult, particularly when quantifying surveillance risk with associated uncertainties. This problem indicates that alternate or improved techniques are required to interpret alarms when incorporating uncertainties and previous knowledge of health incidents into the model to inform decision-making. Such methods must be capable of retaining historical outbreaks to assess surveillance risk. In this research work, the Stochastic Quantitative Risk Assessment (SQRA) model was proposed and developed for detecting and quantifying the risk of disease outbreaks with associated uncertainties using the Bayesian probabilistic approach in PyMC3. A systematic and comparative evaluation of the available techniques was used to select the most appropriate method and software packages based on flexibility, efficiency, usability, ability to retain historical outbreaks, and the ease of developing a model in Python. The social media datasets (Twitter) were first applied to infer a possible disease outbreak incident with associated uncertainties. Then, the inferences were subsequently updated using datasets from the clinical and other healthcare sources to reduce uncertainties in the model and validate the outbreak. Therefore, the proposed SQRA model demonstrates an approach that uses the successive refinement of analysis of different data streams to define a changepoint signalling a disease outbreak. The SQRA model was tested and validated to show the method's effectiveness and reliability for differentiating and identifying risk regions with corresponding changepoints to interpret an ongoing disease outbreak incident. This demonstrates that a technique such as the SQRA method obtained through this research may aid in overcoming some of the difficulties identified in VSyS, such as data scarcity, delayed reporting, and variable availability of data from sources, ultimately contributing to science and practice

Changepoint detection for data intensive settings

Detecting a point in a data sequence where the behaviour alters abruptly, otherwise known as a changepoint, has been an active area of interest for decades. More recently, with the advent of the data intensive era, the need for automated and computationally efficient changepoint methods has grown. We here introduce several new techniques for doing this which address many of the issues inherent in detecting changes in a streaming setting. In short, these new methods, which may be viewed as non-trivial extensions of existing classical procedures, are intended to be as useful in as wide a set of situations as possible, while retaining important theoretical guarantees and ease of implementation. The first novel contribution concerns two methods for parallelising existing dynamic programming based approaches to changepoint detection in the single variate setting. We demonstrate that these methods can result in near quadratic computational gains, while retaining important theoretical guarantees. Our next area of focus is the multivariate setting. We introduce two new methods for data intensive scenarios with a fixed, but possibly large, number of dimensions. The first of these is an offline method which detects one change at a time using a new test statistic. We demonstrate that this test statistic has competitive power in a variety of possible settings for a given changepoint, while allowing the method to be versatile across a range of possible modelling assumptions. The other method we introduce for multivariate data is also suitable in the streaming setting. In addition, it is able to relax many standard modelling assumptions. We discuss the empirical properties of the procedure, especially insofar as they relate to a desired false alarm error rate

Interpretable Goal-based Prediction and Planning for Autonomous Driving

We propose an integrated prediction and planning system for autonomous driving which uses rational inverse planning to recognise the goals of other vehicles. Goal recognition informs a Monte Carlo Tree Search (MCTS) algorithm to plan optimal maneuvers for the ego vehicle. Inverse planning and MCTS utilise a shared set of defined maneuvers and macro actions to construct plans which are explainable by means of \emph{rationality} principles. Evaluation in simulations of urban driving scenarios demonstrate the system's ability to robustly recognise the goals of other vehicles, enabling our vehicle to exploit non-trivial opportunities to significantly reduce driving times. In each scenario, we extract intuitive explanations for the predictions which justify the system's decisions