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Academic literature on the topic 'Robotique centrée sur l'humain'
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Journal articles on the topic "Robotique centrée sur l'humain"
GOCKO, x. "Un agent conversationnel empathique ?" EXERCER 34, no. 195 (September 1, 2023): 291. http://dx.doi.org/10.56746/exercer.2023.195.291.
Full textDeuff, Dominique, Ioana Ocnarescu, Gentiane Venture, and Isabelle Milleville-Pennel. "yōkobo." .able journal, no. 6 (2023). http://dx.doi.org/10.69564/able.fr.23006.yokobo.
Full textGuenat, Guillaume, and Audrey Hostettler. "“Robots are fun” : rhétorique du jeu dans la promotion de la robotique éducative." Revue pluridisciplinaire d'Education par et pour les Doctorant-e-s 1, no. 2 (January 8, 2024). http://dx.doi.org/10.57154/journals/red.2023.e1332.
Full textBoutrouille, Maxime. "Résumé de thèse. “Vers l’acceptation de robots industriels : conception centrée sur l’opérateur humain d’un logiciel robotique au sein d’une industrie 4.0”." Activites 21-2 (2024). http://dx.doi.org/10.4000/12hv3.
Full textDissertations / Theses on the topic "Robotique centrée sur l'humain"
Dillen, Arnau. "Interface neuronale directe pour applications réelles." Electronic Thesis or Diss., CY Cergy Paris Université, 2024. http://www.theses.fr/2024CYUN1295.
Full textSince the inception of digital computers, creating intuitive user interfaces has been crucial. Effective and efficient user interfaces ensure usability, significantly influenced by the deployment environment and target demographic. Diverse interaction modalities are essential for inclusive device usability.Brain-computer interfaces (BCIs) enable interaction with devices through neural signals, promising enhanced interaction for individuals with paralysis and improving their autonomy and quality of life. This research project develops a proof-of-concept software using off-the-shelf hardware to control a robotic arm with BCI. The BCI system decodes user intentions from EEG signals to execute commands, focusing on the optimal design of a BCI control system for practical human-robot collaboration.The research established the following key objectives: developing a real-time motor imagery (MI) decoding strategy with fast decoding, minimal computational cost, and low calibration time; designing a control system to address low MI decoding accuracy while enhancing user experience; and developing an evaluation procedure to quantify system performance and inform improvements.The literature review identified issues like the prevalence of offline decoding and lack of standardized evaluation procedures for BCIs, and highlighted the limitations of using deep learning for MI decoding. This prompted a focus on off-the-shelf machine learning methods for EEG decoding.Initial development benchmarked various EEG decoding pipelines for neuroprostheses control, finding that standard common spatial patterns and linear discriminant analysis were practical despite user customization yielding optimal results. Another investigation reduced the number of sensors for MI decoding, using a 64-channel EEG device and demonstrating that reliable MI decoding can be achieved with just eight well-placed sensors. This feasibility of using low-density EEG devices with fewer than 32 electrodes reduces costs.A comprehensive evaluation framework for BCI control systems was developed, ensuring iterative software improvements and participant training. An augmented reality (AR) control system design was also described, integrating visual feedback with real-world overlays via a shared control approach using eye tracking for object selection and computer vision for spatial awareness.A user study compared the developed BCI control system to an eye-tracking-only control system. While eye tracking outperformed the BCI system, the study confirmed the feasibility of the BCI design for real-world applications with potential enhancements.Key findings include:- Eight well-placed EEG sensors are sufficient for adequate decoding performance, with a non-significant decrease in accuracy from 0.67 to 0.65 when reducing from 64 sensors to 8.- A shared control design informed by real-world contexts simplifies BCI decoding, and AR integration enhances the user interface. Only 2 MI classes are needed to achieve a success rate of 0.83 on evaluation tasks.- Despite eye tracking outperforming current BCI systems, BCIs are feasible for real-world use, with significantly higher efficiency in task completion time for the eye-tracking system.- Consumer-grade EEG devices are viable for EEG acquisition in BCI control systems, with all participants using the commercial EEG device successfully completing evaluation tasks, indicating further cost reductions beyond sensor reduction.Future research should integrate advanced EEG decoding methods like deep learning, transfer learning, and continual learning. Gamifying the calibration procedure may yield better training data and make the control system more attractive to users. Closer hardware-software integration through embedded decoding and built-in sensors in AR headsets should lead to a consumer-ready BCI control system for diverse applications
Paris, Jean-Christophe. "Ingénierie cognitive pour l'aide à la conduite automobile de la personne âgée : analyse et modélisation de l'activité de conduite en situation naturelle pour la conception de fonctions de monitorage." Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0425/document.
Full textThis thesis in Cognitics presents a Human Centered Design approach for thedevelopment of future driving assistance systems dedicated to elderly drivers orElderly Adapted Driver Assistance Systems (E-ADAS).To do so, this work relies on a multi-disciplinary approach for data collection andanalysis. Regarding Ergonomics, the aim is to better understand the specificrequirements of this population in order to identify their actual difficulties and actualneeds of assistance. In this frame, 76 drivers (aged from 70 to 87 years old) took partto an on-the-road experiment, driving an instrumented car. The dataset includes2100 km of ecological driving data and 1400 auto-evaluated driving situations,completed by 6 Focus Groups (involving 30 elderly drivers).The second part of this research, relying on Cognitive Engineering, explores thedesign and implementation of monitoring functions based on the aforementioneddataset. The objective is to have real-time models and analytical functions, able to:(1) supervise the driving activity as realized by an elderly driver, (2) taking in toconsideration the driving context or situational risks (3) in order to detect difficulties ordriving errors. Beyond this thesis, these diagnostics will have to be integrated inassistive systems to better adapt their support to the specific needs of elderly drivers.Specific monitoring functions related to basic vehicle control (speed management,lane positioning and headway regulation) are presented. Based on these results,integrated monitoring functions for intersection crossings in Left-Turn manoeuver,highway merging assistance, and, more broadly, lane change assistance areintroduced
Deniel, Jonathan. "Conscience du risque et automatisation de la conduite : de l’analyse des processus cognitifs en situation de changement de voie à la conception centrée sur l’humain d'un système d'automatisation human-like." Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0445.
Full textDriving activity is both widespread and very complex. Among the most frequently executed manœuvres we identified the lane change (LC) with insertion between traffic vehicles. Current improvements in the field of driving automation are progressively paving the way for the gradual automation of the various components of the driving activity, including, among other things, lane changes.Our purposes in this thesis work in cognitive engineering were (1) to better understanding and contribution to the modeling of the driver's cognitive processes at work when making decisions and performing LC in manual driving (especially regarding the subjective risk assessment), in order to feed the development of the gls{COSMODRIVE} model (cites{bellet2003}). (2) Design and evaluate a textit{Human-like} automation capable of executing a LC as a human driver. This second objective also makes it possible to address questions about the acceptance of the behaviour of this type of automations. (3) Study the possible effects induced by the use of this LC automation, on decisions and judgments regarding lane change situations in subsequent manual driving situations.To achieve these objectives, we set up a research protocol (on the V-HCD driving simulation platform) divided into three intertwined experimental phases and involving the same participants for each of them. Participants were first invited to make decisions on LC and to assess the situational risk and acceptability of a hypothetical automated LC system. Then, in the second phase, they were invited to use and evaluate textit{Human-like} LC automation we designed for this experiment. Finally, in the last phase, participants were instructed to drive again manually and to make LC decisions in driving situations that were specially configured to approach their decision threshold (estimated from a direct analysis of their decisions during the first manual driving phase).The results obtained led us to identify a traffic merging strategy within the LC manœuvre allowing us, on the basis of the envelope zones theory, to explain the structure of the LC decisions. Concerning the human-like LC automation we found a better acceptance of the system by the participants than expected, even from the most reluctants to the autonomous vehicle. We also showed a decrease in the risk estimation of the situation and the manoeuvre when it was performed by the automation. We additionnaly validated the textit{Human-like} aspect of our automation's LC trajectory. Finally, we were able to highlight the occurence of an effect induced by the use of the automation during later manual driving LC situations. This induced effect was resulting in a lowering of the decision threshold as well as the risk assessment of the LC manoeuvre to be executed. These results suggest that the human-centred design approach to driving automation systems design is promising in terms of technology acceptance and adoption. However they may present some potential induced "side" effects that will require deeper investigation
Perusseau-Lambert, Alix. "L’interaction tactile pour médier le travail d’accompagnement des personnes avec autisme." Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS322.
Full textThis doctoral work focuses on the design and implementation of tactile edutainment interfaces via a user centered approach applied to Autism Spectrum Disorder (ASD). Individuals with ASD display heterogeneous sensorimotor features. In the tactile modality, some individuals may have a very high threshold of resistance to pain, while others may not be able to bear a caress. Adapted tactile stimulations in a controlled environment can prove beneficial for their wellbeing. Based on a user-centred design approach, a mechatronic device has been designed and implemented to enable healthcare professionnals working with ASD patients to perform kinesthetic tasks adapted to their needs. The prototype affords an effective working surface of 30 by 42 cm that requires users’ motor skills. Following a user-centered approach, two studies have been conducted to identify application opportunities and how to integrate them in professionnal pratice. Additionally, we studied vibrotactile perception in social condition to better understand the cognitive mechanisms involved in the general human population and to guide possible usage of tactile interface for autism. Our results confirmed the relevance of the user-centred approach in an engineering design process, especially regarding sensorial and motor stimulation applications in the context of care-taking for people with ASD
Al, maghraoui Ouail. "Modéliser l'expérience voyageur pour concevoir la mobilité urbaine." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLC006/document.
Full textThis thesis addresses the challenge of designing urban mobility systems. It aims at developing a traveler experience model to help diagnose travel problems in a design approach and improve the relevance of transportation models for travelers. By combining the views of user-experience design and transportation, it helps to deepen the understanding of how travelers experience their journey and especially the problems they face. The first axis of investigation is related to the modeling of the traveler experience to feed a relevant and rich diagnosis of travel problems. In the second axis, travelers are involved, through a grounded theory approach, to identify the problems they encounter when using urban mobility systems, using appropriate stimuli.The third axis introduces travel subjective attributes into transport models to improve their accuracy.This research used action research as a methodology. It combines literature review in design and transportation disciplines, four field observations, fifteen in-depth interviews with transport travelers and experts, five problem-solving workshops, and two experiments, in a cyclical improvement of results. The various uses of the model have led to an in-depth diagnosis of three urban mobility systems (suburban train, on-demand bus, dedicated shuttle) and the development of traveler-centric attributes for an optimization model and a multi-agent simulation that was tested by a survey of over 450 participants
Bertrand, Astrid. "Misplaced trust in AI : the explanation paradox and the human-centric path. A characterisation of the cognitive challenges to appropriately trust algorithmic decisions and applications in the financial sector." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT012.
Full textAs AI is becoming more widespread in our everyday lives, concerns have been raised about comprehending how these opaque structures operate. In response, the research field of explainability (XAI) has developed considerably in recent years. However, little work has studied regulators' need for explainability or considered effects of explanations on users in light of legal requirements for explanations. This thesis focuses on understanding the role of AI explanations to enable regulatory compliance of AI-enhanced systems in financial applications. The first part reviews the challenge of taking into account human cognitive biases in the explanations of AI systems. The analysis provides several directions to better align explainability solutions with people's cognitive processes, including designing more interactive explanations. It then presents a taxonomy of the different ways to interact with explainability solutions. The second part focuses on specific financial contexts. One study takes place in the domain of online recommender systems for life insurance contracts. The study highlights that feature based explanations do not significantly improve non expert users' understanding of the recommendation, nor lead to more appropriate reliance compared to having no explanation at all. Another study analyzes the needs of regulators for explainability in anti-money laundering and financing of terrorism. It finds that supervisors need explanations to establish the reprehensibility of sampled failure cases, or to verify and challenge banks' correct understanding of the AI