20,886 research outputs found
Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control
This paper provides an overview of the current state-of-the-art in selective
harvesting robots (SHRs) and their potential for addressing the challenges of
global food production. SHRs have the potential to increase productivity,
reduce labour costs, and minimise food waste by selectively harvesting only
ripe fruits and vegetables. The paper discusses the main components of SHRs,
including perception, grasping, cutting, motion planning, and control. It also
highlights the challenges in developing SHR technologies, particularly in the
areas of robot design, motion planning and control. The paper also discusses
the potential benefits of integrating AI and soft robots and data-driven
methods to enhance the performance and robustness of SHR systems. Finally, the
paper identifies several open research questions in the field and highlights
the need for further research and development efforts to advance SHR
technologies to meet the challenges of global food production. Overall, this
paper provides a starting point for researchers and practitioners interested in
developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic
Satellite Image Based Cross-view Localization for Autonomous Vehicle
Existing spatial localization techniques for autonomous vehicles mostly use a
pre-built 3D-HD map, often constructed using a survey-grade 3D mapping vehicle,
which is not only expensive but also laborious. This paper shows that by using
an off-the-shelf high-definition satellite image as a ready-to-use map, we are
able to achieve cross-view vehicle localization up to a satisfactory accuracy,
providing a cheaper and more practical way for localization. While the
utilization of satellite imagery for cross-view localization is an established
concept, the conventional methodology focuses primarily on image retrieval.
This paper introduces a novel approach to cross-view localization that departs
from the conventional image retrieval method. Specifically, our method develops
(1) a Geometric-align Feature Extractor (GaFE) that leverages measured 3D
points to bridge the geometric gap between ground and overhead views, (2) a
Pose Aware Branch (PAB) adopting a triplet loss to encourage pose-aware feature
extraction, and (3) a Recursive Pose Refine Branch (RPRB) using the
Levenberg-Marquardt (LM) algorithm to align the initial pose towards the true
vehicle pose iteratively. Our method is validated on KITTI and Ford Multi-AV
Seasonal datasets as ground view and Google Maps as the satellite view. The
results demonstrate the superiority of our method in cross-view localization
with median spatial and angular errors within meter and ,
respectively.Comment: Accepted by ICRA202
Security and Privacy Problems in Voice Assistant Applications: A Survey
Voice assistant applications have become omniscient nowadays. Two models that
provide the two most important functions for real-life applications (i.e.,
Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR)
models and Speaker Identification (SI) models. According to recent studies,
security and privacy threats have also emerged with the rapid development of
the Internet of Things (IoT). The security issues researched include attack
techniques toward machine learning models and other hardware components widely
used in voice assistant applications. The privacy issues include technical-wise
information stealing and policy-wise privacy breaches. The voice assistant
application takes a steadily growing market share every year, but their privacy
and security issues never stopped causing huge economic losses and endangering
users' personal sensitive information. Thus, it is important to have a
comprehensive survey to outline the categorization of the current research
regarding the security and privacy problems of voice assistant applications.
This paper concludes and assesses five kinds of security attacks and three
types of privacy threats in the papers published in the top-tier conferences of
cyber security and voice domain.Comment: 5 figure
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
The Viability and Potential Consequences of IoT-Based Ransomware
With the increased threat of ransomware and the substantial growth of the Internet of Things (IoT) market, there is significant motivation for attackers to carry out IoT-based ransomware campaigns. In this thesis, the viability of such malware is tested.
As part of this work, various techniques that could be used by ransomware developers to attack commercial IoT devices were explored. First, methods that attackers could use to communicate with the victim were examined, such that a ransom note was able to be reliably sent to a victim. Next, the viability of using "bricking" as a method of ransom was evaluated, such that devices could be remotely disabled unless the victim makes a payment to the attacker. Research was then performed to ascertain whether it was possible to remotely gain persistence on IoT devices, which would improve the efficacy of existing ransomware methods, and provide opportunities for more advanced ransomware to be created. Finally, after successfully identifying a number of persistence techniques, the viability of privacy-invasion based ransomware was analysed.
For each assessed technique, proofs of concept were developed. A range of devices -- with various intended purposes, such as routers, cameras and phones -- were used to test the viability of these proofs of concept. To test communication hijacking, devices' "channels of communication" -- such as web services and embedded screens -- were identified, then hijacked to display custom ransom notes. During the analysis of bricking-based ransomware, a working proof of concept was created, which was then able to remotely brick five IoT devices. After analysing the storage design of an assortment of IoT devices, six different persistence techniques were identified, which were then successfully tested on four devices, such that malicious filesystem modifications would be retained after the device was rebooted. When researching privacy-invasion based ransomware, several methods were created to extract information from data sources that can be commonly found on IoT devices, such as nearby WiFi signals, images from cameras, or audio from microphones. These were successfully implemented in a test environment such that ransomable data could be extracted, processed, and stored for later use to blackmail the victim.
Overall, IoT-based ransomware has not only been shown to be viable but also highly damaging to both IoT devices and their users. While the use of IoT-ransomware is still very uncommon "in the wild", the techniques demonstrated within this work highlight an urgent need to improve the security of IoT devices to avoid the risk of IoT-based ransomware causing havoc in our society. Finally, during the development of these proofs of concept, a number of potential countermeasures were identified, which can be used to limit the effectiveness of the attacking techniques discovered in this PhD research
MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning
We study how a principal can efficiently and effectively intervene on the
rewards of a previously unseen learning agent in order to induce desirable
outcomes. This is relevant to many real-world settings like auctions or
taxation, where the principal may not know the learning behavior nor the
rewards of real people. Moreover, the principal should be few-shot adaptable
and minimize the number of interventions, because interventions are often
costly. We introduce MERMAIDE, a model-based meta-learning framework to train a
principal that can quickly adapt to out-of-distribution agents with different
learning strategies and reward functions. We validate this approach
step-by-step. First, in a Stackelberg setting with a best-response agent, we
show that meta-learning enables quick convergence to the theoretically known
Stackelberg equilibrium at test time, although noisy observations severely
increase the sample complexity. We then show that our model-based meta-learning
approach is cost-effective in intervening on bandit agents with unseen
explore-exploit strategies. Finally, we outperform baselines that use either
meta-learning or agent behavior modeling, in both -shot and -shot
settings with partial agent information
Exploiting Symmetry and Heuristic Demonstrations in Off-policy Reinforcement Learning for Robotic Manipulation
Reinforcement learning demonstrates significant potential in automatically
building control policies in numerous domains, but shows low efficiency when
applied to robot manipulation tasks due to the curse of dimensionality. To
facilitate the learning of such tasks, prior knowledge or heuristics that
incorporate inherent simplification can effectively improve the learning
performance. This paper aims to define and incorporate the natural symmetry
present in physical robotic environments. Then, sample-efficient policies are
trained by exploiting the expert demonstrations in symmetrical environments
through an amalgamation of reinforcement and behavior cloning, which gives the
off-policy learning process a diverse yet compact initiation. Furthermore, it
presents a rigorous framework for a recent concept and explores its scope for
robot manipulation tasks. The proposed method is validated via two
point-to-point reaching tasks of an industrial arm, with and without an
obstacle, in a simulation experiment study. A PID controller, which tracks the
linear joint-space trajectories with hard-coded temporal logic to produce
interim midpoints, is used to generate demonstrations in the study. The results
of the study present the effect of the number of demonstrations and quantify
the magnitude of behavior cloning to exemplify the possible improvement of
model-free reinforcement learning in common manipulation tasks. A comparison
study between the proposed method and a traditional off-policy reinforcement
learning algorithm indicates its advantage in learning performance and
potential value for applications
Bounding Box Annotation with Visible Status
Training deep-learning-based vision systems requires the manual annotation of
a significant amount of data to optimize several parameters of the deep
convolutional neural networks. Such manual annotation is highly time-consuming
and labor-intensive. To reduce this burden, a previous study presented a fully
automated annotation approach that does not require any manual intervention.
The proposed method associates a visual marker with an object and captures it
in the same image. However, because the previous method relied on moving the
object within the capturing range using a fixed-point camera, the collected
image dataset was limited in terms of capturing viewpoints. To overcome this
limitation, this study presents a mobile application-based free-viewpoint
image-capturing method. With the proposed application, users can collect
multi-view image datasets automatically that are annotated with bounding boxes
by moving the camera. However, capturing images through human involvement is
laborious and monotonous. Therefore, we propose gamified application features
to track the progress of the collection status. Our experiments demonstrated
that using the gamified mobile application for bounding box annotation, with
visible collection progress status, can motivate users to collect multi-view
object image datasets with less mental workload and time pressure in an
enjoyable manner, leading to increased engagement.Comment: 10 pages, 16 figure
Passive Radio Frequency-based 3D Indoor Positioning System via Ensemble Learning
Passive radio frequency (PRF)-based indoor positioning systems (IPS) have
attracted researchers' attention due to their low price, easy and customizable
configuration, and non-invasive design. This paper proposes a PRF-based
three-dimensional (3D) indoor positioning system (PIPS), which is able to use
signals of opportunity (SoOP) for positioning and also capture a scenario
signature. PIPS passively monitors SoOPs containing scenario signatures through
a single receiver. Moreover, PIPS leverages the Dynamic Data Driven
Applications System (DDDAS) framework to devise and customize the sampling
frequency, enabling the system to use the most impacted frequency band as the
rated frequency band. Various regression methods within three ensemble learning
strategies are used to train and predict the receiver position. The PRF
spectrum of 60 positions is collected in the experimental scenario, and three
criteria are applied to evaluate the performance of PIPS. Experimental results
show that the proposed PIPS possesses the advantages of high accuracy,
configurability, and robustness.Comment: DDDAS 202
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