534 research outputs found
Conversations on Empathy
In the aftermath of a global pandemic, amidst new and ongoing wars, genocide, inequality, and staggering ecological collapse, some in the public and political arena have argued that we are in desperate need of greater empathy — be this with our neighbours, refugees, war victims, the vulnerable or disappearing animal and plant species. This interdisciplinary volume asks the crucial questions: How does a better understanding of empathy contribute, if at all, to our understanding of others? How is it implicated in the ways we perceive, understand and constitute others as subjects? Conversations on Empathy examines how empathy might be enacted and experienced either as a way to highlight forms of otherness or, instead, to overcome what might otherwise appear to be irreducible differences. It explores the ways in which empathy enables us to understand, imagine and create sameness and otherness in our everyday intersubjective encounters focusing on a varied range of "radical others" – others who are perceived as being dramatically different from oneself. With a focus on the importance of empathy to understand difference, the book contends that the role of empathy is critical, now more than ever, for thinking about local and global challenges of interconnectedness, care and justice
The Emotions Behind the Screen
This thesis revolves around the narrative form of Dungeons and Dragons, that the players have true free will, which allows for the highest level of storytelling. By reaching this level, the players are able to freely tackle issues in their life and surrounding them, like testing out negative parts of their personality and finding community during events like the COVID pandemic. This, combined with the recent scandal from DnD’s publisher Wizards of the Coast poses this question: what exactly is DnD, therapy, art, community, or just a game? This thesis hopes to lead the reader to answer this question in their own terms
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
Learning Motion Skills for a Humanoid Robot
This thesis investigates the learning of motion skills for humanoid robots. As groundwork, a humanoid robot with integrated fall management was developed as an experimental platform. Then, two different approaches for creating motion skills were investigated. First, one that is based on Cartesian quintic splines with optimized parameters.
Second, a reinforcement learning-based approach that utilizes the first approach as a reference motion to guide the learning. Both approaches were tested on the developed robot and on further simulated robots to show their generalization. A special focus was set on the locomotion skill, but a standing-up and kick skill are also discussed.
Diese Dissertation beschäftigt sich mit dem Lernen von Bewegungsfähigkeiten für humanoide Roboter. Als Grundlage wurde zunächst ein humanoider Roboter mit integriertem Fall Management entwickelt, welcher als Experimentalplatform dient. Dann wurden zwei verschiedene Ansätze für die Erstellung von Bewegungsfähigkeiten untersucht. Zu erst einer der kartesische quintische Splines mit optimierten Parametern nutzt.
Danach wurde ein Ansatz basierend auf bestärkendem Lernen untersucht, welcher den ersten Ansatz als Referenzbewegung benutzt. Beide Ansätze wurden sowohl auf der entwickelten Roboterplatform, als auch auf weiteren simulierten Robotern getestet um die Generalisierbarkeit zu zeigen. Ein besonderer Fokus wurde auf die Fähigkeit des Gehens gelegt, aber auch Aufsteh- und Schussfähigkeiten werden diskutiert
Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design
Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data
More Than Machines?
We know that robots are just machines. Why then do we often talk about them as if they were alive? Laura Voss explores this fascinating phenomenon, providing a rich insight into practices of animacy (and inanimacy) attribution to robot technology: from science-fiction to robotics R&D, from science communication to media discourse, and from the theoretical perspectives of STS to the cognitive sciences. Taking an interdisciplinary perspective, and backed by a wealth of empirical material, Voss shows how scientists, engineers, journalists - and everyone else - can face the challenge of robot technology appearing »a little bit alive« with a reflexive and yet pragmatic stance
DEsignBench: Exploring and Benchmarking DALL-E 3 for Imagining Visual Design
We introduce DEsignBench, a text-to-image (T2I) generation benchmark tailored
for visual design scenarios. Recent T2I models like DALL-E 3 and others, have
demonstrated remarkable capabilities in generating photorealistic images that
align closely with textual inputs. While the allure of creating visually
captivating images is undeniable, our emphasis extends beyond mere aesthetic
pleasure. We aim to investigate the potential of using these powerful models in
authentic design contexts. In pursuit of this goal, we develop DEsignBench,
which incorporates test samples designed to assess T2I models on both "design
technical capability" and "design application scenario." Each of these two
dimensions is supported by a diverse set of specific design categories. We
explore DALL-E 3 together with other leading T2I models on DEsignBench,
resulting in a comprehensive visual gallery for side-by-side comparisons. For
DEsignBench benchmarking, we perform human evaluations on generated images in
DEsignBench gallery, against the criteria of image-text alignment, visual
aesthetic, and design creativity. Our evaluation also considers other
specialized design capabilities, including text rendering, layout composition,
color harmony, 3D design, and medium style. In addition to human evaluations,
we introduce the first automatic image generation evaluator powered by GPT-4V.
This evaluator provides ratings that align well with human judgments, while
being easily replicable and cost-efficient. A high-resolution version is
available at
https://github.com/design-bench/design-bench.github.io/raw/main/designbench.pdf?download=Comment: Project page at https://design-bench.github.io
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