112 research outputs found
Drive Like a Human: Rethinking Autonomous Driving with Large Language Models
In this paper, we explore the potential of using a large language model (LLM)
to understand the driving environment in a human-like manner and analyze its
ability to reason, interpret, and memorize when facing complex scenarios. We
argue that traditional optimization-based and modular autonomous driving (AD)
systems face inherent performance limitations when dealing with long-tail
corner cases. To address this problem, we propose that an ideal AD system
should drive like a human, accumulating experience through continuous driving
and using common sense to solve problems. To achieve this goal, we identify
three key abilities necessary for an AD system: reasoning, interpretation, and
memorization. We demonstrate the feasibility of employing an LLM in driving
scenarios by building a closed-loop system to showcase its comprehension and
environment-interaction abilities. Our extensive experiments show that the LLM
exhibits the impressive ability to reason and solve long-tailed cases,
providing valuable insights for the development of human-like autonomous
driving. The related code are available at
https://github.com/PJLab-ADG/DriveLikeAHuman
Systematic AI Approach for AGI: Addressing Alignment, Energy, and AGI Grand Challenges
AI faces a trifecta of grand challenges the Energy Wall, the Alignment
Problem and the Leap from Narrow AI to AGI. Contemporary AI solutions consume
unsustainable amounts of energy during model training and daily
operations.Making things worse, the amount of computation required to train
each new AI model has been doubling every 2 months since 2020, directly
translating to increases in energy consumption.The leap from AI to AGI requires
multiple functional subsystems operating in a balanced manner, which requires a
system architecture. However, the current approach to artificial intelligence
lacks system design; even though system characteristics play a key role in the
human brain from the way it processes information to how it makes decisions.
Similarly, current alignment and AI ethics approaches largely ignore system
design, yet studies show that the brains system architecture plays a critical
role in healthy moral decisions.In this paper, we argue that system design is
critically important in overcoming all three grand challenges. We posit that
system design is the missing piece in overcoming the grand challenges.We
present a Systematic AI Approach for AGI that utilizes system design principles
for AGI, while providing ways to overcome the energy wall and the alignment
challenges.Comment: International Journal on Semantic Computing (2024) Categories:
Artificial Intelligence; AI; Artificial General Intelligence; AGI; System
Design; System Architectur
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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
Reconstruction and recognition of confusable models using three-dimensional perception
Perception is one of the key topics in robotics research. It is about the processing
of external sensor data and its interpretation. The necessity of fully autonomous
robots makes it crucial to help them to perform tasks more reliably, flexibly, and
efficiently. As these platforms obtain more refined manipulation capabilities, they
also require expressive and comprehensive environment models: for manipulation
and affordance purposes, their models have to involve each one of the objects
present in the world, coincidentally with their location, pose, shape and other aspects.
The aim of this dissertation is to provide a solution to several of these challenges
that arise when meeting the object grasping problem, with the aim of improving
the autonomy of the mobile manipulator robot MANFRED-2. By the analysis
and interpretation of 3D perception, this thesis covers in the first place the
localization of supporting planes in the scenario. As the environment will contain
many other things apart from the planar surface, the problem within cluttered
scenarios has been solved by means of Differential Evolution, which is a particlebased
evolutionary algorithm that evolves in time to the solution that yields the
cost function lowest value.
Since the final purpose of this thesis is to provide with valuable information for
grasping applications, a complete model reconstructor has been developed. The
proposed method holdsmany features such as robustness against abrupt rotations,
multi-dimensional optimization, feature extensibility, compatible with other scan
matching techniques, management of uncertain information and an initialization
process to reduce convergence timings. It has been designed using a evolutionarybased
scan matching optimizer that takes into account surface features of the object,
global form and also texture and color information.
The last tackled challenge regards the recognition problem. In order to procure
with worthy information about the environment to the robot, a meta classifier that discerns efficiently the observed objects has been implemented. It is capable
of distinguishing between confusable objects, such as mugs or dishes with similar
shapes but different size or color.
The contributions presented in this thesis have been fully implemented and
empirically evaluated in the platform. A continuous grasping pipeline covering
from perception to grasp planning including visual object recognition for confusable
objects has been developed. For that purpose, an indoor environment with
several objects on a table is presented in the nearby of the robot. Items are recognized
from a database and, if one is chosen, the robot will calculate how to grasp
it taking into account the kinematic restrictions associated to the anthropomorphic
hand and the 3D model for this particular object. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------La percepción es uno de los temas más relevantes en el mundo de la investigaci
ón en robótica. Su objetivo es procesar e interpretar los datos recibidos por
un sensor externo. La gran necesidad de desarrollar robots autónomos hace imprescindible
proporcionar soluciones que les permita realizar tareas más precisas,
flexibles y eficientes. Dado que estas plataformas cada día adquieren mejores capacidades
para manipular objetos, también necesitarán modelos expresivos y comprensivos:
para realizar tareas de manipulación y prensión, sus modelos han de
tener en cuenta cada uno de los objetos presentes en su entorno, junto con su localizaci
ón, orientación, forma y otros aspectos.
El objeto de la presente tesis doctoral es proponer soluciones a varios de los
retos que surgen al enfrentarse al problema del agarre, con el propósito final de
aumentar la capacidad de autonomía del robot manipulador MANFRED-2. Mediante
el análisis e interpretación de la percepción tridimensional, esta tesis cubre
en primer lugar la localización de planos de soporte en sus alrededores. Dado que
el entorno contendrá muchos otros elementos aparte de la superficie de apoyo buscada, el problema en entornos abarrotados ha sido solucionado mediante Evolución
Diferencial, que es un algoritmo evolutivo basado en partículas que evoluciona
temporalmente a la solución que contempla el menor resultado en la función de
coste.
Puesto que el propósito final de este trabajo de investigación es proveer de información valiosa a las aplicaciones de prensión, se ha desarrollado un reconstructor
de modelos completos. El método propuesto posee diferentes características
como robustez a giros abruptos, optimización multidimensional, extensión a otras
características, compatibilidad con otras técnicas de reconstrucción, manejo de incertidumbres
y un proceso de inicialización para reducir el tiempo de convergencia. Ha sido diseñado usando un registro optimizado mediante técnicas evolutivas
que tienen en cuenta las particularidades de la superficie del objeto, su forma
global y la información relativa a la textura.
El último problema abordado está relacionado con el reconocimiento de objetos. Con la intención de abastecer al robot con la mayor información posible sobre el entorno, se ha implementado un meta clasificador que diferencia de manera eficaz los objetos observados. Ha sido capacitado para distinguir objetos confundibles como tazas o platos con formas similares pero con diferentes colores o tamaños.
Las contribuciones presentes en esta tesis han sido completamente implementadas y probadas de manera empírica en la plataforma. Se ha desarrollado un sistema que cubre el problema de agarre desde la percepción al cálculo de la trayectoria
incluyendo el sistema de reconocimiento de objetos confundibles. Para ello, se ha presentado una mesa con objetos en un entorno cerrado cercano al robot. Los elementos son comparados con una base de datos y si se desea agarrar uno de ellos,
el robot estimará cómo cogerlo teniendo en cuenta las restricciones cinemáticas asociadas a una mano antropomórfica y el modelo tridimensional generado del objeto en cuestión
Grounding the Interaction : Knowledge Management for Interactive Robots
Avec le développement de la robotique cognitive, le besoin d’outils avancés pour représenter, manipuler, raisonner sur les connaissances acquises par un robot a clairement été mis en avant. Mais stocker et manipuler des connaissances requiert tout d’abord d’éclaircir ce que l’on nomme connaissance pour un robot, et comment celle-ci peut-elle être représentée de manière intelligible pour une machine. \ud
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Ce travail s’efforce dans un premier temps d’identifier de manière systématique les besoins en terme de représentation de connaissance des applications robotiques modernes, dans le contexte spécifique de la robotique de service et des interactions homme-robot. Nous proposons une typologie originale des caractéristiques souhaitables des systèmes de représentation des connaissances, appuyée sur un état de l’art détaillé des outils existants dans notre communauté. \ud
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Dans un second temps, nous présentons en profondeur ORO, une instanciation particulière d’un système de représentation et manipulation des connaissances, conçu et implémenté durant la préparation de cette thèse. Nous détaillons le fonctionnement interne du système, ainsi que son intégration dans plusieurs architectures robotiques complètes. Un éclairage particulier est donné sur la modélisation de la prise de perspective dans le contexte de l’interaction, et de son interprétation en terme de théorie de l’esprit. \ud
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La troisième partie de l’étude porte sur une application importante des systèmes de représentation des connaissances dans ce contexte de l’interaction homme-robot : le traitement du dialogue situé. Notre approche et les algorithmes qui amènent à l’ancrage interactif de la communication verbale non contrainte sont présentés, suivis de plusieurs expériences menées au Laboratoire d’Analyse et d’Architecture des Systèmes au CNRS à Toulouse, et au groupe Intelligent Autonomous System de l’université technique de Munich. Nous concluons cette thèse sur un certain nombre de considérations sur la viabilité et l’importance d’une gestion explicite des connaissances des agents, ainsi que par une réflexion sur les éléments encore manquant pour réaliser le programme d’une robotique “de niveau humain”.-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------With the rise of the so-called cognitive robotics, the need of advanced tools to store, manipulate, reason about the knowledge acquired by the robot has been made clear. But storing and manipulating knowledge requires first to understand what the knowledge itself means to the robot and how to represent it in a machine-processable way. \ud
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This work strives first at providing a systematic study of the knowledge requirements of modern robotic applications in the context of service robotics and human-robot interaction. What are the expressiveness requirement for a robot? what are its needs in term of reasoning techniques? what are the requirement on the robot's knowledge processing structure induced by other cognitive functions like perception or decision making? We propose a novel typology of desirable features for knowledge representation systems supported by an extensive review of existing tools in our community. \ud
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In a second part, the thesis presents in depth a particular instantiation of a knowledge representation and manipulation system called ORO, that has been designed and implemented during the preparation of the thesis. We elaborate on the inner working of this system, as well as its integration into several complete robot control stacks. A particular focus is given to the modelling of agent-dependent symbolic perspectives and their relations to theories of mind. \ud
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The third part of the study is focused on the presentation of one important application of knowledge representation systems in the human-robot interaction context: situated dialogue. Our approach and associated algorithms leading to the interactive grounding of unconstrained verbal communication are presented, followed by several experiments that have taken place both at the Laboratoire d'Analyse et d'Architecture des Systèmes at CNRS, Toulouse and at the Intelligent Autonomous System group at Munich Technical University. \ud
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The thesis concludes on considerations regarding the viability and importance of an explicit management of the agent's knowledge, along with a reflection on the missing bricks in our research community on the way towards "human level robots". \ud
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Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
cii Student Papers - 2022
In this collection of papers, we, the Research Group Critical Information Infrastructures (cii) from the Karlsruhe Institute of Technology, present eight selected student research articles contributing to the design, development, and evaluation of critical information infrastructures. During our courses, students mostly work in groups and deal with problems and issues related to sociotechnical challenges in the realm of (critical) information systems. Student papers came from five different cii courses, namely Emerging Trends in Internet Technologies, Emerging Trends in Digital Health, Digital Health, Critical Information Infrastructures, and Selected Issues on Critical Information Infrastructures: Collaborative Development of Innovative Teaching Concepts in summer term of 2021 and the winter term of 2021/2022
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