76 research outputs found
Co-manipulation of soft-materials estimating deformation from depth images
Human-robot co-manipulation of soft materials, such as fabrics, composites,
and sheets of paper/cardboard, is a challenging operation that presents several
relevant industrial applications. Estimating the deformation state of the
co-manipulated material is one of the main challenges. Viable methods provide
the indirect measure by calculating the human-robot relative distance. In this
paper, we develop a data-driven model to estimate the deformation state of the
material from a depth image through a Convolutional Neural Network (CNN).
First, we define the deformation state of the material as the relative
roto-translation from the current robot pose and a human grasping position. The
model estimates the current deformation state through a Convolutional Neural
Network, specifically a DenseNet-121 pretrained on ImageNet.The delta between
the current and the desired deformation state is fed to the robot controller
that outputs twist commands. The paper describes the developed approach to
acquire, preprocess the dataset and train the model. The model is compared with
the current state-of-the-art method based on a skeletal tracker from cameras.
Results show that our approach achieves better performances and avoids the
various drawbacks caused by using a skeletal tracker.Finally, we also studied
the model performance according to different architectures and dataset
dimensions to minimize the time required for dataset acquisitionComment: Pre-print, submitted to Journal of Intelligent Manufacturin
Deep Learning of Force Manifolds from the Simulated Physics of Robotic Paper Folding
Robotic manipulation of slender objects is challenging, especially when the
induced deformations are large and nonlinear. Traditionally, learning-based
control approaches, such as imitation learning, have been used to address
deformable material manipulation. These approaches lack generality and often
suffer critical failure from a simple switch of material, geometric, and/or
environmental (e.g., friction) properties. This article tackles a fundamental
but difficult deformable manipulation task: forming a predefined fold in paper
with only a single manipulator. A data-driven framework combining
physically-accurate simulation and machine learning is used to train a deep
neural network capable of predicting the external forces induced on the
manipulated paper given a grasp position. We frame the problem using scaling
analysis, resulting in a control framework robust against material and
geometric changes. Path planning is then carried out over the generated "neural
force manifold" to produce robot manipulation trajectories optimized to prevent
sliding, with offline trajectory generation finishing 15 faster than
previous physics-based folding methods. The inference speed of the trained
model enables the incorporation of real-time visual feedback to achieve
closed-loop sensorimotor control. Real-world experiments demonstrate that our
framework can greatly improve robotic manipulation performance compared to
state-of-the-art folding strategies, even when manipulating paper objects of
various materials and shapes.Comment: Supplementary video is available on YouTube:
https://youtu.be/k0nexYGy-P
Real-time Motion Generation and Data Augmentation for Grasping Moving Objects with Dynamic Speed and Position Changes
While deep learning enables real robots to perform complex tasks had been
difficult to implement in the past, the challenge is the enormous amount of
trial-and-error and motion teaching in a real environment. The manipulation of
moving objects, due to their dynamic properties, requires learning a wide range
of factors such as the object's position, movement speed, and grasping timing.
We propose a data augmentation method for enabling a robot to grasp moving
objects with different speeds and grasping timings at low cost. Specifically,
the robot is taught to grasp an object moving at low speed using teleoperation,
and multiple data with different speeds and grasping timings are generated by
down-sampling and padding the robot sensor data in the time-series direction.
By learning multiple sensor data in a time series, the robot can generate
motions while adjusting the grasping timing for unlearned movement speeds and
sudden speed changes. We have shown using a real robot that this data
augmentation method facilitates learning the relationship between object
position and velocity and enables the robot to perform robust grasping motions
for unlearned positions and objects with dynamically changing positions and
velocities
Chapter 34 - Biocompatibility of nanocellulose: Emerging biomedical applications
Nanocellulose already proved to be a highly relevant material for biomedical
applications, ensued by its outstanding mechanical properties and, more importantly, its biocompatibility. Nevertheless, despite their previous intensive
research, a notable number of emerging applications are still being developed.
Interestingly, this drive is not solely based on the nanocellulose features, but also
heavily dependent on sustainability. The three core nanocelluloses encompass
cellulose nanocrystals (CNCs), cellulose nanofibrils (CNFs), and bacterial nanocellulose (BNC). All these different types of nanocellulose display highly interesting biomedical properties per se, after modification and when used in
composite formulations. Novel applications that use nanocellulose includewell-known areas, namely, wound dressings, implants, indwelling medical
devices, scaffolds, and novel printed scaffolds. Their cytotoxicity and biocompatibility using recent methodologies are thoroughly analyzed to reinforce their
near future applicability. By analyzing the pristine core nanocellulose, none
display cytotoxicity. However, CNF has the highest potential to fail long-term
biocompatibility since it tends to trigger inflammation. On the other hand, neverdried BNC displays a remarkable biocompatibility. Despite this, all nanocelluloses clearly represent a flag bearer of future superior biomaterials, being
elite materials in the urgent replacement of our petrochemical dependence
Producing Humans: An Anthropology of Social and Cognitive Robots
In this thesis, I ask how the human is produced in robotics research,
focussing specifically on the work that is done to create humanoid robots
that exhibit social and intelligent behaviour. Robots, like other technologies,
are often presented as the result of the systematic application of progressive
scientific knowledge over time, and thus emerging as inevitable, ahistorical,
and a-territorial entities. However, as we shall see, the robot’s existence as a
recognisable whole, as well as the various ways in which researchers
attempt to shape, animate and imbue it ‘human-like’ qualities, is in fact the
result of specific events, in specific geographical and cultural locations.
Through an ethnographic investigation of the sites in which robotics
research takes place, I describe and analyse how, in robotics research,
robotics researchers are reflecting, reproducing, producing, and sometimes
challenging, core assumptions about what it means to be human.
The dissertation draws on three and a half years of ethnographic
research across a number of robotics research laboratories and field sites in
Ireland, the United Kingdom, and the United States between April 2016 and
December 2019. It also includes an investigation of the sites where robotics
knowledge is disseminated and evaluated, such as conferences and field test
sites. Through a combination of participant and non-participant observation,
interviews, and textual analysis, I explore how the robot reveals
assumptions about the human, revealing both individual, localised
engineering cultures, as well as wider Euro-American imaginaries.
In this dissertation, I build on existing ethnographies of laboratory
work and technological production, which investigate scientific laboratories
as cultural sites. I also contribute to contemporary debates in anthropology
and posthumanist theory, which question the foundational assumptions of
humanism. While contemporary scholarship has attempted to move beyond
the nature/culture binary by articulating a multitude of reconfigurations and
boundary negotiations, I argue that this is done by neglecting the body.
In order to address this gap, I bring together two complementary
conceptual devices. First, I employ the embodiment philosophy of Maurice
Merleau-Ponty (2012; 1968) particularly his emphasis on the body as a site
of knowing the world. Second, I use the core anthropological concept of the
‘fetish’ as elaborated by William Pietz (1985). By interrogating the robot as
‘fetish’, I elaborate how the robot is simultaneously a territorialised,
historicised, personalised, and reified object. This facilitates an exploration
of the disparate, and often contradictory nature, of the relations between
people and objects.
In my thesis, I find many boundary reconfigurations and dissolutions
between the human and the robot. However, deviating from the relational
ontology dominant in the anthropology of technology, I discover an
enduring asymmetry between the human and the robot, with the living body
emerging as a durable category that cannot be reasoned away. Thus, my
thesis questions how the existing literature might obscure important
questions about the category of the human by focusing disproportionately
on the blurring and/or blurred nature of human/non-human boundaries.
Ultimately, I argue for a collaborative and emergent configuration of the
human, and its relationship with the world, that is at once both relational
and embodied.
This dissertation is structured as follows. An initial introductory
chapter is followed by a chapter documenting the literature review and
conceptual framework. This is followed by four chapters that correspond to
the four aspects of the fetish in Pietz’s model: Historicisation,
Territorialisation, Reification and Personalisation. These chapters alternate
between scholarly sources and ethnographic data. In Historicisation, using
existing scholarship, I trace the history of the robot object, including the
continuities and discontinuities that led to its creation, as well as the futures
that are implicated in its identity. This is followed by the Territorialisation
chapter, in which ethnographic data is used to interrogate the robot’s
materiality, as well as the spaces in which it is built, modified, and tested.
The next chapter, Reification, considers the robot as a valuable object
according to institutions and the productive and ideological systems of
Euro-American imaginaries. This chapter integrates ethnographic detail
with existing scholarship to focus on contrasts between the dominant image
of imminent super-human intelligence and the human interventions and
social relationships necessary to produce the illusion of robot autonomy.
Finally, the chapter Personalisation brings ethnographic attention to the
intensely personal way that the robot-as-fetish is experienced in an
encounter with an embodied person, understood through the lens of
Merleau-Ponty’s embodiment philosophy. In the final chapter, I draw
together the various strands to articulate how understanding the robot as a
fetish, underscored by Merleau-Ponty’s embodiment phenomenology, can
provide useful resources for developing an alternative understanding of the
human in anthropology without dissolving it all together
EG-ICE 2021 Workshop on Intelligent Computing in Engineering
The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways
ロボットハンドによる風呂敷包みのほどき作業に関する研究
本研究の目的はロボットハンドによって風呂敷包みの結び目をほどく作業の実現である.人間が風呂敷包みをほどく場合,まず繰り返し「外乱」を与えることで結び目を緩める.その後に結び目の布を掴み,引き抜くことでそれをほどく.本研究ではこの人間による作業を参考にして,ロボットが可能なほどき作業を提案した.具体的には「摘み上げ操作」と「引きずり操作」という2つ操作を定義し,それらを使い分けながら繰り返し用いることで,外乱による漸次的なほどき作業を実現した.「摘み上げ操作」は結び目をほどく上で基本となる操作である.一方の「引きずり操作」は結び目が固く結ばれており,「摘み上げ操作」のみではほどくことが困難な場合に,それを緩めるために用いる操作である.また布は柔軟物であり,そのため結び目の形状は多様である.よって布の変形に対して頑強な,結び目の位置,方向,固さを認識するための手法が必要である.結び目の固さの認識は,「摘み上げ操作」と「引きずり操作」を使い分けるために用いる.本研究ではニューラルネットワークによってこの認識を行った.以上の手法により,77%の割合で風呂敷包みの結び目をほどくことに成功した.電気通信大学202
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