7 research outputs found
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