Learning body models: from humans to humanoids

Humans and animals excel in combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth, failures, or using tools. These capabilities are also highly desirable in robots. They are displayed by machines to some extent. Yet, the artificial creatures are lagging behind. The key foundation is an internal representation of the body that the agent - human, animal, or robot - has developed. The mechanisms of operation of body models in the brain are largely unknown and even less is known about how they are constructed from experience after birth. In collaboration with developmental psychologists, we conducted targeted experiments to understand how infants acquire first "sensorimotor body knowledge". These experiments inform our work in which we construct embodied computational models on humanoid robots that address the mechanisms behind learning, adaptation, and operation of multimodal body representations. At the same time, we assess which of the features of the "body in the brain" should be transferred to robots to give rise to more adaptive and resilient, self-calibrating machines. We extend traditional robot kinematic calibration focusing on self-contained approaches where no external metrology is needed: self-contact and self-observation. Problem formulation allowing to combine several ways of closing the kinematic chain simultaneously is presented, along with a calibration toolbox and experimental validation on several robot platforms. Finally, next to models of the body itself, we study peripersonal space - the space immediately surrounding the body. Again, embodied computational models are developed and subsequently, the possibility of turning these biologically inspired representations into safe human-robot collaboration is studied.Comment: 34 pages, 5 figures. Habilitation thesis, Faculty of Electrical Engineering, Czech Technical University in Prague (2021

What do we learn about development from baby robots?

Understanding infant development is one of the greatest scientific challenges of contemporary science. A large source of difficulty comes from the fact that the development of skills in infants results from the interactions of multiple mechanisms at multiple spatio-temporal scales. The concepts of "innate" or "acquired" are not any more adequate tools for explanations, which call for a shift from reductionist to systemic accounts. To address this challenge, building and experimenting with robots modeling the growing infant brain and body is crucial. Systemic explanations of pattern formation in sensorimotor, cognitive and social development, viewed as a complex dynamical system, require the use of formal models based on mathematics, algorithms and robots. Formulating hypothesis about development using such models, and exploring them through experiments, allows us to consider in detail the interaction between many mechanisms and parameters. This complements traditional experimental methods in psychology and neuroscience where only a few variables can be studied at the same time. Furthermore, the use of robots is of particular importance. The laws of physics generate everywhere around us spontaneous patterns in the inorganic world. They also strongly impact the living, and in particular constrain and guide infant development through the properties of its (changing) body in interaction with the physical environment. Being able to consider the body as an experimental variable, something that can be systematically changed in order to study the impact on skill formation, has been a dream to many developmental scientists. This is today becoming possible with developmental robotics

Artificial intelligence applications in marketing: the chatbot of the Department of Economics and Management "Marco Fanno”

openL'intelligenza artificiale (AI) offre numerose applicazioni nel marketing, ma allo stesso tempo ci sono diverse limitazioni da considerare nella sua adozione. Dopo la prima parte di analisi generale delle applicazioni e degli aspetti negativi dell'AI e dei chatbot, la tesi si concentra sul caso dell'implementazione di un chatbot da parte del Dipartimento di Economia e Management “Marco Fanno” dell'Università di Padova. La domanda di ricerca è volta a capire se il chatbot implementato dal Dipartimento sia stato efficace nell'alleggerire e supportare il lavoro dell'ufficio amministrativo e nel rispondere alle domande degli studenti. A tal fine, il documento analizza se il numero di email è diminuito dopo l'introduzione del chatbot. Inoltre è stato svolto un questionario per valutare l'esperienza che gli studenti del Dipartimento hanno avuto con il chatbot di ateneo. Il sondaggio ha anche chiesto agli studenti quali servizi vorrebbero che il chatbot aggiungesse a quelli attuali. Inoltre, è stata condotta un'analisi economica su benefici e costi per valutare se il chatbot genererà un risultato economico positivo. Questo studio consente di valutare l'impatto che un chatbot potrebbe avere nel campo dell'istruzione. In particolare, può fornire informazioni alle università sul fatto che un chatbot possa migliorare il coinvolgimento con gli studenti, liberare il personale da compiti ripetitivi e generare benefici economici netti nel lungo periodo. Il questionario stesso è stato condotto attraverso un sondaggio web su Google Forms e un sondaggio attraverso un chatbot. In questo modo ho anche analizzato quale dei due metodi sia il più efficace per condurre un'indagine. Alcune prove rivelano come i sondaggi condotti attraverso un chatbot possano portare a risposte più accurate da parte degli intervistati. Confrontando i risultati ottenuti della due modalità di sondaggio ho potuto verificare queste evidenze con un nuovo campione di partecipanti, gli studenti di Economia. I risultati della tesi non hanno mostrato prove chiare del fatto che il chatbot consentisse di ridurre il numero di e-mail. Ma si suggerisce un'indagine su un periodo più lungo. Successivamente i risultati hanno evidenziato un buon apprezzamento degli studenti per il chatbot e hanno suggerito l'introduzione di notifiche push che ricordano delle scadenze universitarie come le tasse. La stima dell'analisi costi-benefici prevedeva un risultato netto positivo su tre anni con un ROI del 29%. Inoltre, il sondaggio chatbot ha parzialmente confermato la tendenza ad ottenere risposte più accurate rispetto ad un classico sondaggio web.Artificial intelligence (AI) offers numerous applications in marketing, but at the same time, there are several limitations to consider in its adoption. After the first part about a general analysis of the applications and negative aspects of AI and chatbots, the thesis focuses on the case of the implementation of a chatbot by the Department of Economics and Management “Marco Fanno” of the University of Padua. The research question turns towards understanding whether the chatbot implemented by the Department was effective in easing and supporting the work of the administrative office and answering students questions. For this purpose, the paper analyses if the number of emails is decreased after the chatbot introduction. In addition, a questionnaire was carried out to evaluate the experience that the students of the Department have had with the university chatbot. The survey also asked students what services they would like the chatbot to add to their current ones. Moreover, an economic analysis on benefits and costs was conducted to estimate whether the chatbot will generate a positive outcome. This study allows evaluating the impact a chatbot could have in the education field. In particular, it can provide insight to universities on whether a chatbot could enhance the engagement with students, offload staff from repetitive tasks and generate net economic benefits in the long period. The questionnaire itself was conducted through a web survey on Google Forms and a chatbot survey. In this way, it could also be verified which of the two methods is the most effective to conduct a survey. Some evidence finds how chatbot surveys can lead to less satisfactory answers by respondents. Comparing the two survey results, I can verify these past findings with a different sample of participants, the students of Economics. The results did not show clear evidence of whether the chatbot allowed reducing the number of emails. But an investigation over a longer period is suggested. Then, findings highlighted a good appreciation of students for the chatbot and suggested the introduction of push notifications that remember university deadlines such as taxes. The estimation of the benefits-cost analysis forecasted a net positive outcome over three years with an ROI of 29%. Also, the chatbot survey partially confirmed the encouraging finding in reducing satisficing by respondents.

Vision-based Robotic Grasping in Simulation using Deep Reinforcement Learning

This thesis will investigate different robotic manipulation and grasping approaches. It will present an overview of robotic simulation environments, and offer an evaluation of PyBullet, CoppeliaSim, and Gazebo, comparing various features. The thesis further presents a background for current approaches to robotic manipulation and grasping by describing how the robotic movement and grasping can be organized. State-of-the-Art approaches for learning robotic grasping, both using supervised methods and reinforcement learning methods are presented. Two set of experiments will be conducted in PyBullet, illustrating how Deep Reinforcement Learning methods could be applied to train a 7 degrees of freedom robotic arm to grasp objects

MIMo: A Multi-Modal Infant Model for Studying Cognitive Development

Human intelligence and human consciousness emerge gradually during the process of cognitive development. Understanding this development is an essential aspect of understanding the human mind and may facilitate the construction of artificial minds with similar properties. Importantly, human cognitive development relies on embodied interactions with the physical and social environment, which is perceived via complementary sensory modalities. These interactions allow the developing mind to probe the causal structure of the world. This is in stark contrast to common machine learning approaches, e.g., for large language models, which are merely passively ``digesting'' large amounts of training data, but are not in control of their sensory inputs. However, computational modeling of the kind of self-determined embodied interactions that lead to human intelligence and consciousness is a formidable challenge. Here we present MIMo, an open-source multi-modal infant model for studying early cognitive development through computer simulations. MIMo's body is modeled after an 18-month-old child with detailed five-fingered hands. MIMo perceives its surroundings via binocular vision, a vestibular system, proprioception, and touch perception through a full-body virtual skin, while two different actuation models allow control of his body. We describe the design and interfaces of MIMo and provide examples illustrating its use. All code is available at https://github.com/trieschlab/MIMo .Comment: 11 pages, 8 figures. Submitted to IEEE Transactions on Congnitive and Developmental Systems (TCDS