Generative neural data synthesis for autonomous systems

A significant number of Machine Learning methods for automation currently rely on data-hungry training techniques. The lack of accessible training data often represents an insurmountable obstacle, especially in the fields of robotics and automation, where acquiring new data can be far from trivial. Additional data acquisition is not only often expensive and time-consuming, but occasionally is not even an option. Furthermore, the real world applications sometimes have commercial sensitivity issues associated with the distribution of the raw data. This doctoral thesis explores bypassing the aforementioned difficulties by synthesising new realistic and diverse datasets using the Generative Adversarial Network (GAN). The success of this approach is demonstrated empirically through solving a variety of case-specific data-hungry problems, via application of novel GAN-based techniques and architectures. Specifically, it starts with exploring the use of GANs for the realistic simulation of the extremely high-dimensional underwater acoustic imagery for the purpose of training both teleoperators and autonomous target recognition systems. We have developed a method capable of generating realistic sonar data of any chosen dimension by image-translation GANs with Markov principle. Following this, we apply GAN-based models to robot behavioural repertoire generation, that enables a robot manipulator to successfully overcome unforeseen impedances, such as unknown sets of obstacles and random broken joints scenarios. Finally, we consider dynamical system identification for articulated robot arms. We show how using diversity-driven GAN models to generate exploratory trajectories can allow dynamic parameters to be identified more efficiently and accurately than with conventional optimisation approaches. Together, these results show that GANs have the potential to benefit a variety of robotics learning problems where training data is currently a bottleneck

Behavioral Repertoire via Generative Adversarial Policy Networks

Learning algorithms are enabling robots to solve increasingly challenging real-world tasks. These approaches often rely on demonstrations and reproduce the behavior shown. Unexpected changes in the environment may require using different behaviors to achieve the same effect, for instance to reach and grasp an object in changing clutter. An emerging paradigm addressing this robustness issue is to learn a diverse set of successful behaviors for a given task, from which a robot can select the most suitable policy when faced with a new environment. In this paper, we explore a novel realization of this vision by learning a generative model over policies. Rather than learning a single policy, or a small fixed repertoire, our generative model for policies compactly encodes an unbounded number of policies and allows novel controller variants to be sampled. Leveraging our generative policy network, a robot can sample novel behaviors until it finds one that works for a new environment. We demonstrate this idea with an application of robust ball-throwing in the presence of obstacles. We show that this approach achieves a greater diversity of behaviors than an existing evolutionary approach, while maintaining good efficacy of sampled behaviors, allowing a Baxter robot to hit targets more often when ball throwing in the presence of obstacles.Comment: In Proceedings of 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pages 320 - 32

Full-Scale Continuous Synthetic Sonar Data Generation with Markov Conditional Generative Adversarial Networks

Deployment and operation of autonomous underwater vehicles is expensive and time-consuming. High-quality realistic sonar data simulation could be of benefit to multiple applications, including training of human operators for post-mission analysis, as well as tuning and validation of autonomous target recognition (ATR) systems for underwater vehicles. Producing realistic synthetic sonar imagery is a challenging problem as the model has to account for specific artefacts of real acoustic sensors, vehicle altitude, and a variety of environmental factors. We propose a novel method for generating realistic-looking sonar side-scans of full-length missions, called Markov Conditional pix2pix (MC-pix2pix). Quantitative assessment results confirm that the quality of the produced data is almost indistinguishable from real. Furthermore, we show that bootstrapping ATR systems with MC-pix2pix data can improve the performance. Synthetic data is generated 18 times faster than real acquisition speed, with full user control over the topography of the generated data.Comment: 6 pages, 6 figures. Accepted to ICRA2020. 2020 IEEE International Conference on Robotics and Automatio

Adversarial Generation of Informative Trajectories for Dynamics System Identification

Dynamic System Identification approaches usually heavily rely on the evolutionary and gradient-based optimisation techniques to produce optimal excitation trajectories for determining the physical parameters of robot platforms. Current optimisation techniques tend to generate single trajectories. This is expensive, and intractable for longer trajectories, thus limiting their efficacy for system identification. We propose to tackle this issue by using multiple shorter cyclic trajectories, which can be generated in parallel, and subsequently combined together to achieve the same effect as a longer trajectory. Crucially, we show how to scale this approach even further by increasing the generation speed and quality of the dataset through the use of generative adversarial network (GAN) based architectures to produce a large databases of valid and diverse excitation trajectories. To the best of our knowledge, this is the first robotics work to explore system identification with multiple cyclic trajectories and to develop GAN-based techniques for scaleably producing excitation trajectories that are diverse in both control parameter and inertial parameter spaces. We show that our approach dramatically accelerates trajectory optimisation, while simultaneously providing more accurate system identification than the conventional approach.Comment: Accepted for publication in IEEE iROS 202

Survey: Leakage and Privacy at Inference Time

Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance as commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data. We provide a comprehensive survey of contemporary advances on several fronts, covering involuntary data leakage which is natural to ML models, potential malevolent leakage which is caused by privacy attacks, and currently available defence mechanisms. We focus on inference-time leakage, as the most likely scenario for publicly available models. We first discuss what leakage is in the context of different data, tasks, and model architectures. We then propose a taxonomy across involuntary and malevolent leakage, available defences, followed by the currently available assessment metrics and applications. We conclude with outstanding challenges and open questions, outlining some promising directions for future research