435 research outputs found
Artificial Intelligence and International Conflict in Cyberspace
This edited volume explores how artificial intelligence (AI) is transforming international conflict in cyberspace. Over the past three decades, cyberspace developed into a crucial frontier and issue of international conflict. However, scholarly work on the relationship between AI and conflict in cyberspace has been produced along somewhat rigid disciplinary boundaries and an even more rigid sociotechnical divide â wherein technical and social scholarship are seldomly brought into a conversation. This is the first volume to address these themes through a comprehensive and cross-disciplinary approach. With the intent of exploring the question âwhat is at stake with the use of automation in international conflict in cyberspace through AI?â, the chapters in the volume focus on three broad themes, namely: (1) technical and operational, (2) strategic and geopolitical and (3) normative and legal. These also constitute the three parts in which the chapters of this volume are organised, although these thematic sections should not be considered as an analytical or a disciplinary demarcation
From Vision-Language Multimodal Learning Towards Embodied Agents
To build machine agents with intelligent capabilities mimicking human perception and cognition, vision and language stand out as two essential modalities and foster computer vision and natural language processing. Advances in such realms stimulate research in vision-language multimodal learning that allows optical and linguistic inputs and outputs. Due to the innate difference between the two modalities and the lack of large-scale fine-grained annotations, multimodal agents tend to inherit unimodal shortcuts. In this thesis, we develop various solutions to intervene unimodal shortcuts for multimodal generation and reasoning. For visual shortcuts, we introduce a linguistic prior and devise a syntax-aware action targeting module for dynamic description to rectify the correlation between subject and object in a sentence. We apply concept hierarchy and propose a visual superordinate abstraction framework for unbiased concept learning to reduce the correlation among different attributes of an object. For linguistic shortcuts, we disentangle the topic and syntax to reduce the repetition in generated paragraph descriptions for a given image. With the ubiquity of large-scale pre-trained models, we leverage self-supervised learning in finetuning process to increase the robustness of multimodal reasoning.
The rapid development in multimodal learning promises embodied agents capable of interacting with physical environments. This thesis studies the typical embodied task vision-and-language navigation in discrete scenarios and proposes an episodic scene memory (ESceme) mechanism to balance generalization and efficiency. We figure out one desirable instantiation of the mechanism, namely candidate enhancing, and validate its superiority in various settings. Without extra time and computational cost before inference, ESceme improves performance in unseen environments by a large margin. We hope our findings can inspire more practical explorations on episodic memory in embodied AI
Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology
The great behavioral heterogeneity observed between individuals with the same
psychiatric disorder and even within one individual over time complicates both
clinical practice and biomedical research. However, modern technologies are an
exciting opportunity to improve behavioral characterization. Existing
psychiatry methods that are qualitative or unscalable, such as patient surveys
or clinical interviews, can now be collected at a greater capacity and analyzed
to produce new quantitative measures. Furthermore, recent capabilities for
continuous collection of passive sensor streams, such as phone GPS or
smartwatch accelerometer, open avenues of novel questioning that were
previously entirely unrealistic. Their temporally dense nature enables a
cohesive study of real-time neural and behavioral signals.
To develop comprehensive neurobiological models of psychiatric disease, it
will be critical to first develop strong methods for behavioral quantification.
There is huge potential in what can theoretically be captured by current
technologies, but this in itself presents a large computational challenge --
one that will necessitate new data processing tools, new machine learning
techniques, and ultimately a shift in how interdisciplinary work is conducted.
In my thesis, I detail research projects that take different perspectives on
digital psychiatry, subsequently tying ideas together with a concluding
discussion on the future of the field. I also provide software infrastructure
where relevant, with extensive documentation.
Major contributions include scientific arguments and proof of concept results
for daily free-form audio journals as an underappreciated psychiatry research
datatype, as well as novel stability theorems and pilot empirical success for a
proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop
2023-2024 Lindenwood University Undergraduate Course Catalog
Lindenwood University Undergraduate Course Catalog.https://digitalcommons.lindenwood.edu/catalogs/1209/thumbnail.jp
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 beschĂ€ftigt sich mit dem Lernen von BewegungsfĂ€higkeiten fĂŒr humanoide Roboter. Als Grundlage wurde zunĂ€chst ein humanoider Roboter mit integriertem Fall Management entwickelt, welcher als Experimentalplatform dient. Dann wurden zwei verschiedene AnsĂ€tze fĂŒr die Erstellung von BewegungsfĂ€higkeiten untersucht. Zu erst einer der kartesische quintische Splines mit optimierten Parametern nutzt.
Danach wurde ein Ansatz basierend auf bestÀrkendem Lernen untersucht, welcher den ersten Ansatz als Referenzbewegung benutzt. Beide AnsÀtze wurden sowohl auf der entwickelten Roboterplatform, als auch auf weiteren simulierten Robotern getestet um die Generalisierbarkeit zu zeigen. Ein besonderer Fokus wurde auf die FÀhigkeit des Gehens gelegt, aber auch Aufsteh- und SchussfÀhigkeiten werden diskutiert
Evolutionary Reinforcement Learning: A Survey
Reinforcement learning (RL) is a machine learning approach that trains agents
to maximize cumulative rewards through interactions with environments. The
integration of RL with deep learning has recently resulted in impressive
achievements in a wide range of challenging tasks, including board games,
arcade games, and robot control. Despite these successes, there remain several
crucial challenges, including brittle convergence properties caused by
sensitive hyperparameters, difficulties in temporal credit assignment with long
time horizons and sparse rewards, a lack of diverse exploration, especially in
continuous search space scenarios, difficulties in credit assignment in
multi-agent reinforcement learning, and conflicting objectives for rewards.
Evolutionary computation (EC), which maintains a population of learning agents,
has demonstrated promising performance in addressing these limitations. This
article presents a comprehensive survey of state-of-the-art methods for
integrating EC into RL, referred to as evolutionary reinforcement learning
(EvoRL). We categorize EvoRL methods according to key research fields in RL,
including hyperparameter optimization, policy search, exploration, reward
shaping, meta-RL, and multi-objective RL. We then discuss future research
directions in terms of efficient methods, benchmarks, and scalable platforms.
This survey serves as a resource for researchers and practitioners interested
in the field of EvoRL, highlighting the important challenges and opportunities
for future research. With the help of this survey, researchers and
practitioners can develop more efficient methods and tailored benchmarks for
EvoRL, further advancing this promising cross-disciplinary research field
Evaluating footwear âin the wildâ: Examining wrap and lace trail shoe closures during trail running
Trail running participation has grown over the last two decades. As a result, there have been an increasing number of studies examining the sport. Despite these increases, there is a lack of understanding regarding the effects of footwear on trail running biomechanics in ecologically valid conditions. The purpose of our study was to evaluate how a Wrap vs. Lace closure (on the same shoe) impacts running biomechanics on a trail. Thirty subjects ran a trail loop in each shoe while wearing a global positioning system (GPS) watch, heart rate monitor, inertial measurement units (IMUs), and plantar pressure insoles. The Wrap closure reduced peak foot eversion velocity (measured via IMU), which has been associated with fit. The Wrap closure also increased heel contact area, which is also associated with fit. This increase may be associated with the subjective preference for the Wrap. Lastly, runners had a small but significant increase in running speed in the Wrap shoe with no differences in heart rate nor subjective exertion. In total, the Wrap closure fit better than the Lace closure on a variety of terrain. This study demonstrates the feasibility of detecting meaningful biomechanical differences between footwear features in the wild using statistical tools and study design. Evaluating footwear in ecologically valid environments often creates additional variance in the data. This variance should not be treated as noise; instead, it is critical to capture this additional variance and challenges of ecologically valid terrain if we hope to use biomechanics to impact the development of new products
Video Conferencing: Infrastructures, Practices, Aesthetics
The COVID-19 pandemic has reorganized existing methods of exchange, turning comparatively marginal technologies into the new normal. Multipoint videoconferencing in particular has become a favored means for web-based forms of remote communication and collaboration without physical copresence. Taking the recent mainstreaming of videoconferencing as its point of departure, this anthology examines the complex mediality of this new form of social interaction. Connecting theoretical reflection with material case studies, the contributors question practices, politics and aesthetics of videoconferencing and the specific meanings it acquires in different historical, cultural and social contexts
Learning-based methods for planning and control of humanoid robots
Nowadays, humans and robots are more and more likely to coexist as time goes by. The anthropomorphic nature of humanoid robots facilitates physical human-robot interaction, and makes social human-robot interaction more natural. Moreover, it makes humanoids ideal candidates for many applications related to tasks and environments designed for humans.
No matter the application, an ubiquitous requirement for the humanoid is to possess proper locomotion skills. Despite long-lasting research, humanoid locomotion is still far from being a trivial task. A common approach to address humanoid locomotion consists in decomposing its complexity by means of a model-based hierarchical control architecture. To cope with computational constraints, simplified models for the humanoid are employed in some of the architectural layers. At the same time, the redundancy of the humanoid with respect to the locomotion task as well as the closeness of such a task to human locomotion suggest a data-driven approach to learn it directly from experience.
This thesis investigates the application of learning-based techniques to planning and control of humanoid locomotion. In particular, both deep reinforcement learning and deep supervised learning are considered to address humanoid locomotion tasks in a crescendo of complexity.
First, we employ deep reinforcement learning to study the spontaneous emergence of balancing and push recovery strategies for the humanoid, which represent essential prerequisites for more complex locomotion tasks.
Then, by making use of motion capture data collected from human subjects, we employ deep supervised learning to shape the robot walking trajectories towards an improved human-likeness.
The proposed approaches are validated on real and simulated humanoid robots. Specifically, on two versions of the iCub humanoid: iCub v2.7 and iCub v3
Versatile Multi-Contact Planning and Control for Legged Loco-Manipulation
Loco-manipulation planning skills are pivotal for expanding the utility of
robots in everyday environments. These skills can be assessed based on a
system's ability to coordinate complex holistic movements and multiple contact
interactions when solving different tasks. However, existing approaches have
been merely able to shape such behaviors with hand-crafted state machines,
densely engineered rewards, or pre-recorded expert demonstrations. Here, we
propose a minimally-guided framework that automatically discovers whole-body
trajectories jointly with contact schedules for solving general
loco-manipulation tasks in pre-modeled environments. The key insight is that
multi-modal problems of this nature can be formulated and treated within the
context of integrated Task and Motion Planning (TAMP). An effective bilevel
search strategy is achieved by incorporating domain-specific rules and
adequately combining the strengths of different planning techniques: trajectory
optimization and informed graph search coupled with sampling-based planning. We
showcase emergent behaviors for a quadrupedal mobile manipulator exploiting
both prehensile and non-prehensile interactions to perform real-world tasks
such as opening/closing heavy dishwashers and traversing spring-loaded doors.
These behaviors are also deployed on the real system using a two-layer
whole-body tracking controller
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