8 research outputs found
FabricFolding: Learning Efficient Fabric Folding without Expert Demonstrations
Autonomous fabric manipulation is a challenging task due to complex dynamics
and potential self-occlusion during fabric handling. An intuitive method of
fabric folding manipulation first involves obtaining a smooth and unfolded
fabric configuration before the folding process begins. However, the
combination of quasi-static actions such as pick & place and dynamic action
like fling proves inadequate in effectively unfolding long-sleeved T-shirts
with sleeves mostly tucked inside the garment. To address this limitation, this
paper introduces an improved quasi-static action called pick & drag,
specifically designed to handle this type of fabric configuration.
Additionally, an efficient dual-arm manipulation system is designed in this
paper, which combines quasi-static (including pick & place and pick & drag) and
dynamic fling actions to flexibly manipulate fabrics into unfolded and smooth
configurations. Subsequently, keypoints of the fabric are detected, enabling
autonomous folding. To address the scarcity of publicly available keypoint
detection datasets for real fabric, we gathered images of various fabric
configurations and types in real scenes to create a comprehensive keypoint
dataset for fabric folding. This dataset aims to enhance the success rate of
keypoint detection. Moreover, we evaluate the effectiveness of our proposed
system in real-world settings, where it consistently and reliably unfolds and
folds various types of fabrics, including challenging situations such as
long-sleeved T-shirts with most parts of sleeves tucked inside the garment.
Specifically, our method achieves a coverage rate of 0.822 and a success rate
of 0.88 for long-sleeved T-shirts folding
Swarm Metaverse for Multi-Level Autonomy Using Digital Twins
Robot swarms are becoming popular in domains that require spatial coordination. Effective human control over swarm members is pivotal for ensuring swarm behaviours align with the dynamic needs of the system. Several techniques have been proposed for scalable human–swarm interaction. However, these techniques were mostly developed in simple simulation environments without guidance on how to scale them up to the real world. This paper addresses this research gap by proposing a metaverse for scalable control of robot swarms and an adaptive framework for different levels of autonomy. In the metaverse, the physical/real world of a swarm symbiotically blends with a virtual world formed from digital twins representing each swarm member and logical control agents. The proposed metaverse drastically decreases swarm control complexity due to human reliance on only a few virtual agents, with each agent dynamically actuating on a sub-swarm. The utility of the metaverse is demonstrated by a case study where humans controlled a swarm of uncrewed ground vehicles (UGVs) using gestural communication, and via a single virtual uncrewed aerial vehicle (UAV). The results show that humans could successfully control the swarm under two different levels of autonomy, while task performance increases as autonomy increases.</p
Investigating Human Perceptions of Trust and Social Cues in Robots for Safe Human-Robot Interaction in Human-oriented Environments
As robots increasingly take part in daily living activities, humans will have to
interact with them in domestic and other human-oriented environments. This thesis
envisages a future where autonomous robots could be used as home companions
to assist and collaborate with their human partners in unstructured environments
without the support of any roboticist or expert. To realise such a vision, it is important
to identify which factors (e.g. trust, participants’ personalities and background
etc.) that influence people to accept robots’ as companions and trust the robots to
look after their well-being. I am particularly interested in the possibility of robots
using social behaviours and natural communications as a repair mechanism to
positively influence humans’ sense of trust and companionship towards the robots.
The main reason being that trust can change over time due to different factors
(e.g. perceived erroneous robot behaviours). In this thesis, I provide guidelines
for a robot to regain human trust by adopting certain human-like behaviours. I
can expect that domestic robots will exhibit occasional mechanical, programming
or functional errors, as occurs with any other electrical consumer devices. For
example, these might include software errors, dropping objects due to gripper
malfunctions, picking up the wrong object or showing faulty navigational skills due
to unclear camera images or noisy laser scanner data respectively. It is therefore
important for a domestic robot to have acceptable interactive behaviour when
exhibiting and recovering from an error situation. In this context, several open
questions need to be addressed regarding both individuals’ perceptions of the errors
and robots, and the effects of these on people’s trust in robots.
As a first step, I investigated how the severity of the consequences and the timing
of a robot’s different types of erroneous behaviours during an interaction may have
different impact on users’ attitudes towards a domestic robot. I concluded that
there is a correlation between the magnitude of an error performed by the robot and
the corresponding loss of trust of the human in the robot. In particular, people’s
trust was strongly affected by robot errors that had severe consequences.
This led us to investigate whether people’s awareness of robots’ functionalities may
affect their trust in a robot. I found that people’s acceptance and trust in the robot
may be affected by their knowledge of the robot’s capabilities and its limitations
differently according the participants’ age and the robot’s embodiment.
In order to deploy robots in the wild, strategies for mitigating and re-gaining
people’s trust in robots in case of errors needs to be implemented. In the following
three studies, I assessed if a robot with awareness of human social conventions
would increase people’s trust in the robot. My findings showed that people almost
blindly trusted a social and a non-social robot in scenarios with non-severe error
consequences. In contrast, people that interacted with a social robot did not trust
its suggestions in a scenario with a higher risk outcome.
Finally, I investigated the effects of robots’ errors on people’s trust of a robot over
time. The findings showed that participants’ judgement of a robot is formed during
the first stage of their interaction. Therefore, people are more inclined to lose trust
in a robot if it makes big errors at the beginning of the interaction.
The findings from the Human-Robot Interaction experiments presented in this
thesis will contribute to an advanced understanding of the trust dynamics between
humans and robots for a long-lasting and successful collaboration
UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments
The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection
Data ethics : building trust : how digital technologies can serve humanity
Data is the magic word of the 21st century. As oil in the 20th century and electricity in the 19th century:
For citizens, data means support in daily life in almost all activities, from watch to laptop, from kitchen to car,
from mobile phone to politics. For business and politics, data means power, dominance, winning the race. Data can be used for good and bad,
for services and hacking, for medicine and arms race. How can we build trust in this complex and ambiguous data world?
How can digital technologies serve humanity? The 45 articles in this book represent a broad range of ethical reflections and recommendations
in eight sections: a) Values, Trust and Law, b) AI, Robots and Humans, c) Health and Neuroscience, d) Religions for Digital Justice, e) Farming, Business, Finance, f) Security, War, Peace, g) Data Governance, Geopolitics, h) Media, Education, Communication.
The authors and institutions come from all continents.
The book serves as reading material for teachers, students, policy makers, politicians, business, hospitals, NGOs and religious organisations alike. It is an invitation for dialogue, debate and building trust!
The book is a continuation of the volume “Cyber Ethics 4.0” published in 2018 by the same editors
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