Gianluca baldassarre

you tell you mistaken. Not essence..

Gianluca baldassarre

Imagine a friend asking for help to tidy up her room that is full of objects and furniture. Now imagine for some reason your friend will not be there to help we are all lazy and she just describes, showing photos, the way she would like the room to look.

Although it may be a boring task, everyone could handle this with ease. As little kids, we discovered new objects and learned to recognise them and developed new skills to manipulate these objects. Guided by our curiosity, we progressively developed visual, attentional, and sensorimotor knowledge that allows us, as adults, to manipulate our physical environment as desired. Current robots are ill-suited for these challenges. Imagine a humanoid robot helping to clean a room. Assume you have shown the robot the room in its normal tidy state, but once it is messy you tell the robot to make it tidy, as it was before.

In such conditions, it would be tedious to teach the robot where to allocate attention and show how each object has to be manipulated to put it in its desired place and orientation, or how to sequence the different actions to do so. Although new sophisticated robots and powerful algorithms are developed each year, carrying out complex duties and finding unknown solutions to different tasks still require tedious programming of low-level motor control details.

If we compare current artificial agents to biological ones, we find that artificial ones still present relevant limitations in terms of autonomy and versatility.

G. Baldassarre

This will allow them, with little additional learning, to change an environment from its current state to a wide range of potential goal states desired by the user. The question is: how can we create future robots that are able to face this challenge?

The scientific investigation of IMs began by observing how children, driven by curiosity, explore and interact with the world, acquiring knowledge of how things work and an ample repertoire of sensorimotor skills to act on it. GOAL-Robots also adds a new critical component for the development of open-ended learning in robots: goals. Firstly, the agent can activate this representation even in the absence of the perception of the corresponding world state or event.

The possibility of creating motivating goals at will, even abstract ones, and use them to guide actions and learning is a key element of the behavioural flexibility and learning power of biological agents. The bet of GOAL Robots is that endowing robots with suitable mechanisms to form and exploit goals for learning will drastically increase their autonomous learning potential. The possibility of creating and motivating goals at will, even abstract ones, and use them to guide actions and learning is a key element of the behavioural flexibility and learning power of biological agents.

GOAL-Robots bets that endowing robots with suitable mechanisms to form and use goals will drastically increase their autonomous learning potential. The robots will thus be driven to explore the environment by their curiosity and to self-generate increasingly complex goals, using them to learn numerous skills in an open-ended fashion.

The open-ended process of competence acquisition requires sophisticated mechanisms and the integration of different architectural components. In particular, the robots will need to acquire new skills without impairing previously learned ones and, at the same time, to re-use previously acquired skills to speed up the acquisition of skills for new goals knowledge transfer. Moreover, they will need to be able to compose previously acquired skills to form increasingly complex skills.

These issues represent some of the most important current challenges of artificial intelligence. To face them, the project will use state-of-the-art algorithms to face both the processing of sensory information e. Moreover, all the mechanisms related to the different components of the learning process will need to be integrated within a single architecture controlling the robots: the high-level goal-formation processes will be connected to motivational layers where, on the basis of IMs, the robot will form and select goals to focus on; goals will be gradually connected to the low-levels of the controllers so that the robot will be able to recall the learned skills to accomplish desired goals or to form more complex skills by composing them; knowledge transfer between different skills will be integrated with the avoidance of their interference, and so on.

These mechanisms will be useful not only for the autonomous learning phase but also to allow an external user to exploit the knowledge and competence acquired by the robots. In particular, the robots will be requested to: a observe an ordered scenario formed by several objects located in containers and shelves on a working plane, and b reproduce such state of the environment after a user misplaces and mixes the objects.It has become clear to researchers in robotics and adaptive behaviour that current approaches are yielding systems with limited autonomy and capacity for self-improvement.

Intrinsic motivations continue during adulthood, and in humans they underlie lifelong learning, artistic creativity, and scientific discovery, while they are also the basis for processes that strongly affect human well-being, such as the sense of competence, self-determination, and self-esteem. This book has two aims: to present the state of the art in research on intrinsically motivated learning, and to identify the related scientific and technological open challenges and most promising research directions.

The book introduces the concept of intrinsic motivation in artificial systems, reviews the relevant literature, offers insights from the neural and behavioural sciences, and presents novel tools for research. The book is organized into six parts: the chapters in Part I give general overviews on the concept of intrinsic motivations, their function, and possible mechanisms for implementing them; Parts II, III, and IV focus on three classes of intrinsic motivation mechanisms, those based on predictors, on novelty, and on competence; Part V discusses mechanisms that are complementary to intrinsic motivations; and Part VI introduces tools and experimental frameworks for investigating intrinsic motivations.

The contributing authors are among the pioneers carrying out fundamental work on this topic, drawn from related disciplines such as artificial intelligence, robotics, artificial life, evolution, machine learning, developmental psychology, cognitive science, and neuroscience. The book will be of value to graduate students and academic researchers in these domains, and to engineers engaged with the design of autonomous, adaptive robots. Gianluca Baldassarre is a Researcher at the Institute of Cognitive Sciences and Technologies ISTC of the Italian National Research Council CNR where he is a member of the Laboratory of Computational Embodied Neuroscience; his research interests include computational embodied neuroscience, psychology, neuroscience, developmental robotics, artificial life, and machine learning.

Marco Mirolli is a Researcher at the Institute of Cognitive Sciences and Technologies ISTC of the Italian National Research Council CNR where he is a member of the Laboratory of Computational Embodied Neuroscience Laboratory; his research interests lie in the study of behavior through computer simulations, in particular the evolution of communication and language, the role of language as a cognitive tool, the biological bases of motivations and emotions, and the role of intrinsic motivations in cumulative learning.

JavaScript is currently disabled, this site works much better if you enable JavaScript in your browser. Computer Science Artificial Intelligence. Free Preview. Interdisciplinary authors explain latest theories on mammalian intelligence and learning, artificial intelligence, creativity, and evolution Identifies scientific and technological open challenges and most promising research directions Grounds theoretical with practical robotics experiments.

Buy eBook. Buy Hardcover. Buy Softcover. FAQ Policy. About this book It has become clear to researchers in robotics and adaptive behaviour that current approaches are yielding systems with limited autonomy and capacity for self-improvement.

Show all. Show next xx. Read this book on SpringerLink. Recommended for you.

gianluca baldassarre

PAGE 1.Autonomous robotics and development with humanoid robots; computational embodied neuroscience; non-supervised, supervised, and reinforcement learning; open-ended learning; intrinsic and extrinsic motivations; goal-directed and habitual behavior; attention and active vision; hierarchical sensorimotor architectures; Pavlovian and instrumental learning; brain: amygdala, hippocampus, basal ganglia, cerebellum, cortex.

These web pages mainly concern my scientific research activities. Since the history of things and people explains a lot of them, here is the informal story of my training and my research experiences. During the university I started attending some courses at the Department of Philosophy, University of Rome Sapienza, as I have always been interested in understanding knowledge and intelligence, the process that produces it. Following some courses, however, I felt that philosophy treated these problems in a way that was not sufficiently scientific at least in Rome and so subsequently I followed courses in the Department of Psychology, University of Rome Sapienza.

Here, thanks to the fascinating lessons of Prof. Eliano Pessa, I learned the computational approach to the study of intelligence and in particular I was "struck" by neural networks, the model of artificial intelligence that best represents "learning", intended as a process of generation and acquisition of knowledge, a theme on which I have ever since dedicated my research starting from the final research thesis in Economics where I simulated with neural networks a group of agents forming an oligopolistic market.

Later I started a three-year "PhD in Computer Science" at the University of Essex Colcester, United Kingdom where I focused on "planning with neural networks and reinforcement learning" under the guidance of Prof. Jim Doran, one of the researchers who contributed to early developments in Classical Artificial Intelligence in Britain.

Before, during and after my doctorate, I continued to study psychology and neuroscience in depth. Here my research involved "embodied systems" of "artificial life" evolutionary approaches with genetic algorithms for the study of self-organised social phenomena through collective robotic systems.

This research reinforced in me the idea that "true" intelligence can only emerge from the interaction of agents with the environment through a body sensors and actuators.

Later I tried to combine this "embodied vision" of intelligence with the study of the brain and behavior through computational models in a way more strongly linked to the empirical data coming from psychology and neurosciences, in particular working in other European projects within which I started working independently MindRaces, ICEA. In I created a research group at the ISTC where I became Researcher in called "LOCEN -Laboratory of Computational Embodied Neuroscience" dedicated to studying behavior and brain through computational models that make a synthesis between "embodied vision" of intelligence and empirical constraints coming from neuroscience and psychology data.

Baldassarre Introduzione Alle Reti Neurali

With LOCEN we progressively specialized in studying the quintessential forms of learning, those related to "curiosity" intrinsic motivations: surprise, novelty, self-generation of objectives, acquisition of competence as those you see in playing children and scientists. These approaches have led us to deepen both the fundamental mechanisms behind natural learning neuroscience and psychology: Pavlovian and instrumental learning, goal-directed and habitual behavior, sensorimotor coordination, attention; brain: hierarchical sensorimotor architectures, motivations, amygdala, hippocampus, basal ganglia, cerebellum, cortex and the mechanisms of learning in artificial intelligence and robotics developmental and autonomous robotics: vision-manipulation coordination, open-ended learning, intrinsic and extrinsic motivations, active vision; machine learning : unsupervised learning, supervised learning, reinforcement learning, classical and "deep" neural networks.

The second objective aims to produce robots able to autonomously learn sensorimotor knowledge and skills in a cumulative way on the basis of intrinsic motivations. In particular, this objective focuses on the development of autonomous robots able to learn to solve many tasks, not just one or a few specific tasks as often happens in robotics.

In addition, robots must learn autonomously: from the point of view of applications, this point is very important to limit the use of human work to program the robots, and also to allow the robots to solve tasks in environments that present challenges that cannot be anticipated at the time of their programming for example, the non-structured environments that are faced by service robots, or the robots that explore new environments such as the space.

The strategy to build these robots is "open learning" based on the self-generation of the objectives tasks on the basis of intrinsic motivations novelty, surprise, acquisition of competence : the self-generated goals then allow the robots to acquire motor skills and models of the world.

This knowledge can then be used by robots to perform tasks useful for human users. At LOCEN we believe that the development of "true" artificial intelligence must pass through these processes.

Sunny phenyl

This is a same story with dates and some more details :. Follow this link to have more details on my research methods and topics:.Skip to search form Skip to main content You are currently offline.

Ti ho voluto bene veramente - Marco Mengoni - Piano Cover by Gianluca Baldassarre

Some features of the site may not work correctly. Publications Citations Highly Influential Citations Follow Author Claim Author Page.

Sku analysis

Author pages are created from data sourced from our academic publisher partnerships and public sources. Recommended Authors. Publications Influence. Has PDF. More Filters. Despite increasing evidence suggesting the cerebellum works in concert with the cortex and basal ganglia, the nature of the reciprocal interactions between these three brain regions remains unclear. View on Springer. Research Feed. This single area perspective gives a restricted clinical picture and limits therapeutic … Expand.

View on Nature. View PDF. Classical and instrumental conditioning : From laboratory phenomena to integrated mechanisms for adaptation. Traditionally classical and instrumental conditioning have been studied in laboratory conditions in great detail but without trying to explain their role in organisms' adaptation. For example, little … Expand. To date, this approach has never been used to deal … Expand.

Decentralized coordination is usually based on self-organizing principles. Very often research on decentralized multi-robot systems makes a general claim on the presence of these principles … Expand. View via Publisher. Although the occurrence of Parkinsonian akinesia and tremor is traditionally associated to dopaminergic degeneration, the multifaceted neural processes that cause these impairments are not fully … Expand. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License.Autonomous robotics and development with humanoid robots; computational embodied neuroscience; non-supervised, supervised, and reinforcement learning; open-ended learning; intrinsic and extrinsic motivations; goal-directed and habitual behavior; attention and active vision; hierarchical sensorimotor architectures; Pavlovian and instrumental learning; brain: amygdala, hippocampus, basal ganglia, cerebellum, cortex.

These web pages mainly concern my scientific research activities. Since the history of things and people explains a lot of them, here is the informal story of my training and my research experiences. During the university I started attending some courses at the Department of Philosophy, University of Rome Sapienza, as I have always been interested in understanding knowledge and intelligence, the process that produces it.

Witcher 3 best combat skills reddit

Following some courses, however, I felt that philosophy treated these problems in a way that was not sufficiently scientific at least in Rome and so subsequently I followed courses in the Department of Psychology, University of Rome Sapienza. Here, thanks to the fascinating lessons of Prof.

Eliano Pessa, I learned the computational approach to the study of intelligence and in particular I was "struck" by neural networks, the model of artificial intelligence that best represents "learning", intended as a process of generation and acquisition of knowledge, a theme on which I have ever since dedicated my research starting from the final research thesis in Economics where I simulated with neural networks a group of agents forming an oligopolistic market.

Later I started a three-year "PhD in Computer Science" at the University of Essex Colcester, United Kingdom where I focused on "planning with neural networks and reinforcement learning" under the guidance of Prof. Jim Doran, one of the researchers who contributed to early developments in Classical Artificial Intelligence in Britain.

Before, during and after my doctorate, I continued to study psychology and neuroscience in depth. Here my research involved "embodied systems" of "artificial life" evolutionary approaches with genetic algorithms for the study of self-organised social phenomena through collective robotic systems.

This research reinforced in me the idea that "true" intelligence can only emerge from the interaction of agents with the environment through a body sensors and actuators.

gianluca baldassarre

Later I tried to combine this "embodied vision" of intelligence with the study of the brain and behavior through computational models in a way more strongly linked to the empirical data coming from psychology and neurosciences, in particular working in other European projects within which I started working independently MindRaces, ICEA.

In I created a research group at the ISTC where I became Researcher in called "LOCEN -Laboratory of Computational Embodied Neuroscience" dedicated to studying behavior and brain through computational models that make a synthesis between "embodied vision" of intelligence and empirical constraints coming from neuroscience and psychology data.

With LOCEN we progressively specialized in studying the quintessential forms of learning, those related to "curiosity" intrinsic motivations: surprise, novelty, self-generation of objectives, acquisition of competence as those you see in playing children and scientists. These approaches have led us to deepen both the fundamental mechanisms behind natural learning neuroscience and psychology: Pavlovian and instrumental learning, goal-directed and habitual behavior, sensorimotor coordination, attention; brain: hierarchical sensorimotor architectures, motivations, amygdala, hippocampus, basal ganglia, cerebellum, cortex and the mechanisms of learning in artificial intelligence and robotics developmental and autonomous robotics: vision-manipulation coordination, open-ended learning, intrinsic and extrinsic motivations, active vision; machine learning : unsupervised learning, supervised learning, reinforcement learning, classical and "deep" neural networks.

The second objective aims to produce robots able to autonomously learn sensorimotor knowledge and skills in a cumulative way on the basis of intrinsic motivations. In particular, this objective focuses on the development of autonomous robots able to learn to solve many tasks, not just one or a few specific tasks as often happens in robotics. In addition, robots must learn autonomously: from the point of view of applications, this point is very important to limit the use of human work to program the robots, and also to allow the robots to solve tasks in environments that present challenges that cannot be anticipated at the time of their programming for example, the non-structured environments that are faced by service robots, or the robots that explore new environments such as the space.

The strategy to build these robots is "open learning" based on the self-generation of the objectives tasks on the basis of intrinsic motivations novelty, surprise, acquisition of competence : the self-generated goals then allow the robots to acquire motor skills and models of the world.

This knowledge can then be used by robots to perform tasks useful for human users. At LOCEN we believe that the development of "true" artificial intelligence must pass through these processes.

This is a same story with dates and some more details :. Follow this link to have more details on my research methods and topics:. LOCEN research method and topics.Autonomous robotics and development with humanoid robots; computational embodied neuroscience; non-supervised, supervised, and reinforcement learning; open-ended learning; intrinsic and extrinsic motivations; goal-directed and habitual behavior; attention and active vision; hierarchical sensorimotor architectures; Pavlovian and instrumental learning; brain: amygdala, hippocampus, basal ganglia, cerebellum, cortex.

These web pages mainly concern my scientific research activities. Since the history of things and people explains a lot of them, here is the informal story of my training and my research experiences. During the university I started attending some courses at the Department of Philosophy, University of Rome Sapienza, as I have always been interested in understanding knowledge and intelligence, the process that produces it.

Following some courses, however, I felt that philosophy treated these problems in a way that was not sufficiently scientific at least in Rome and so subsequently I followed courses in the Department of Psychology, University of Rome Sapienza.

Here, thanks to the fascinating lessons of Prof.

Saucony autunno/inverno 2018 jazz o premium suede running

Eliano Pessa, I learned the computational approach to the study of intelligence and in particular I was "struck" by neural networks, the model of artificial intelligence that best represents "learning", intended as a process of generation and acquisition of knowledge, a theme on which I have ever since dedicated my research starting from the final research thesis in Economics where I simulated with neural networks a group of agents forming an oligopolistic market.

Later I started a three-year "PhD in Computer Science" at the University of Essex Colcester, United Kingdom where I focused on "planning with neural networks and reinforcement learning" under the guidance of Prof. Jim Doran, one of the researchers who contributed to early developments in Classical Artificial Intelligence in Britain. Before, during and after my doctorate, I continued to study psychology and neuroscience in depth.

Here my research involved "embodied systems" of "artificial life" evolutionary approaches with genetic algorithms for the study of self-organised social phenomena through collective robotic systems.

G. Baldassarre

This research reinforced in me the idea that "true" intelligence can only emerge from the interaction of agents with the environment through a body sensors and actuators.

Later I tried to combine this "embodied vision" of intelligence with the study of the brain and behavior through computational models in a way more strongly linked to the empirical data coming from psychology and neurosciences, in particular working in other European projects within which I started working independently MindRaces, ICEA. In I created a research group at the ISTC where I became Researcher in called "LOCEN -Laboratory of Computational Embodied Neuroscience" dedicated to studying behavior and brain through computational models that make a synthesis between "embodied vision" of intelligence and empirical constraints coming from neuroscience and psychology data.

With LOCEN we progressively specialized in studying the quintessential forms of learning, those related to "curiosity" intrinsic motivations: surprise, novelty, self-generation of objectives, acquisition of competence as those you see in playing children and scientists. These approaches have led us to deepen both the fundamental mechanisms behind natural learning neuroscience and psychology: Pavlovian and instrumental learning, goal-directed and habitual behavior, sensorimotor coordination, attention; brain: hierarchical sensorimotor architectures, motivations, amygdala, hippocampus, basal ganglia, cerebellum, cortex and the mechanisms of learning in artificial intelligence and robotics developmental and autonomous robotics: vision-manipulation coordination, open-ended learning, intrinsic and extrinsic motivations, active vision; machine learning : unsupervised learning, supervised learning, reinforcement learning, classical and "deep" neural networks.

The second objective aims to produce robots able to autonomously learn sensorimotor knowledge and skills in a cumulative way on the basis of intrinsic motivations. In particular, this objective focuses on the development of autonomous robots able to learn to solve many tasks, not just one or a few specific tasks as often happens in robotics.

In addition, robots must learn autonomously: from the point of view of applications, this point is very important to limit the use of human work to program the robots, and also to allow the robots to solve tasks in environments that present challenges that cannot be anticipated at the time of their programming for example, the non-structured environments that are faced by service robots, or the robots that explore new environments such as the space.

The strategy to build these robots is "open learning" based on the self-generation of the objectives tasks on the basis of intrinsic motivations novelty, surprise, acquisition of competence : the self-generated goals then allow the robots to acquire motor skills and models of the world. This knowledge can then be used by robots to perform tasks useful for human users.

At LOCEN we believe that the development of "true" artificial intelligence must pass through these processes. This is a same story with dates and some more details :.

Free printable writing paper with borders

Follow this link to have more details on my research methods and topics:. LOCEN research method and topics. Selected publications in the pdf of the papers are downloadable from here :.

Reinforcement-learning, hierarhy, and learning of multiple skills in animals and robots. Intrinsic motivations in animals and robots. Extrinsic motivations in animals and embodied systems.

Collective, evolved, self-organising robots. Thoughts of Gianluca Baldassarre. Here I report thoughts of other people, on science and other issues of life, that I strongly like:. Thoughts of other people that I strongly like. My poems. Skip to main content. Group membership:. Laboratory of Computational Embodied Neuroscience.

Additional details Profile Position:.

gianluca baldassarre

Keywords Autonomous robotics and development with humanoid robots; computational embodied neuroscience; non-supervised, supervised, and reinforcement learning; open-ended learning; intrinsic and extrinsic motivations; goal-directed and habitual behavior; attention and active vision; hierarchical sensorimotor architectures; Pavlovian and instrumental learning; brain: amygdala, hippocampus, basal ganglia, cerebellum, cortex.

Informal history a bit long of my research These web pages mainly concern my scientific research activities.I love reading your things every day. They are helping me grow. Especially as I am now courageous enough to add meditation to my practice. Pingback: What do Stress Reduction and Dementia Have in Common. Pingback: How to Stay in the Now: Personal Growth, Business Growth and Spiritual Growth lead to Real Growth()Thank you for the great set of tips for increasing mindfulness.

Adding a new reminder each week or so, sounds like fun AND a wonderful way to make progress. I am going to give it a try. Thank you for the article.

gianluca baldassarre

It made me realize the things I need to work on. Everything has to be in line or else it makes me an unhappy. I printed it out so it will be a constant reminder for me.

UGH THANK YOU SO MUCH!. I just look into this my stress is causing a lot of health problems I need help. I need a partner. I need to talk to someone to help with this problemGreatGreatGreatAdvice. I had a wonderful day exercising my mindfulness muscle. What a wonderful day. Thank youI feel relieved when I read this. I think if I can do this, the stress in my life will be lessen. I love this i hope it helps me im a mother of 3 who needs me to be there for them and i want to be there when they need meHenry, I enjoyed your article and this is really helpful.

Keep on sharing good things and I appreciate you for helping people around. So what should one do if one steps back, looks at the thought and it is true. I remember my mother used to tell me back in the old days people used to live simple, pure life. As in no luxuries, no desires, just pure and simple. People back home used to live all in one houses like families even if the house was little there was more joy more happiness more success. Now luxury cars, big mansions, and greed for money money and money.

People are starting to think of themselves only and ignore their own loved ones and friends. Going out for a small walk and breathe some fresh air can be do a great deal to tackle stress. Thanks for the article. It helped me a lot to chase my dream.


Gujora

thoughts on “Gianluca baldassarre

Leave a Reply

Your email address will not be published. Required fields are marked *

Back to top