Article written at the occasion of the XRCE 20th anniversary celebration.
The answer may lie in the gap between research and use of technology in the real world.
With their vision naturally dominated by a technological perspective, researchers and software engineers typically do not make good product designers, and in many cases are not a typical end user of the targeted system.
Experience design bridges the gap by focusing on the quality of the user experience. In this way it can be used to leverage innovation to develop new business opportunities and accelerate the commercialisation of disruptive technology.
The bridge between work practice and technology design
The study of work practices is a method of research that reveals opportunities for innovation – innovation that can be easily adopted by real end users. The studies provide:
Selecting the problems experienced in the workplace and how to address them should be the result of collaboration between interaction and experience designers with computer scientists in multidisciplinary teams. Adding a filter based on “out of the box” thinking typical of designers can help to imagine the future. It can be checked for feasibility by the scientists while still being anchored to real world user problems.
But experience design is not risk free, and must be done carefully to avoid the creation of false expectations. A work practice study can have the drawback that, when exposed as input to computer scientists, it provides a large array of options for improvement. Information and Communication Technology is already far from ideal in the workplace for multiple reasons: organisational practices are not well designed to take into account the features of computer systems, or these systems have often been quite simply poorly designed, and so forth. Many of these problems can be solved through better technology design, whilst others could benefit from the latest technological advances. In our research, we are primarily interested in the latter, as it provides the largest potential and longer term benefit to the user.
Why we need designers in scientific research.
Beyond user-centred innovation, where design is based on user observation and requirements, there are situations where researchers need to link technologies to users. This is particularly true with disruptive technologies that can result in new work patterns. Industrial design can help create these links. The challenge is not about making proposals to users, but about imagining new functions or ways they can do their work. This is the sweet spot where industrial design can provide extraordinary insight into the benefits of a disruptive technology, helping users project themselves into a new working environment.
As an example, consider the case of text classification technology. This technology, based on machine learning methods, had been successfully applied for several years as part of document workflow tools, particularly in document imaging and scanning centres. Once scanned the document content is analysed and automatically classified for archiving, retrieval etc. In parallel, alternative paths were explored by research to see how the technology could be used by knowledge workers in completely different environments. Knowledge workers are subject matter experts who manipulate, analyse and understand information in environments where humans are central to the process. The classification technology can be used to support their work but not to fully automate their processes. When imagining a new way of providing the text classifier to such experts, our main insight was that we needed to deploy the technology in a tangible environment. This was realised through the choice of a multi-touch device to make the interaction fast and intuitive, hiding the technicalities of the underlying statistical algorithms. Turning this vision into reality could only happen by integrating industrial design. A team external to the project, with a fresh eye and different mind-set was required. Industrial designers brought aesthetic and communication skills to the table. Combined with R&D expertise, the team was able to creatively innovate upon the underlying technology realised in a unique jointly patented prototype system.
The user interface and system features of this prototype for paralegals in e-discovery can be viewed here.
Using design to promote commercialisation
After the user and the technology comes the third component of experience design – the business. This piece provides the market background and research and potentially the offering as well as the development of a final product.
A common challenge for R&D is to convey the value of what is being proposed. The visual part of experience design can help communicate ideas across organisations and businesses, especially when concepts are disruptive and theoretically complex to understand. Being able to walk the business through different scenarios with hands on experimental interaction with the prototype concept helps them to appropriate the technology. It may even generate new ideas for the offer or identify different market opportunities.
There is however an associated risk with this approach. Providing a well-shaped design proposal and working prototype can mislead the business into thinking it is developed and operational which is far from reality. Without a clear communication strategy false expectations may be generated over availability.
Conclusion
By definition research starts from a blank sheet which may be filled with multiple possibilities and many different paths relating to both a technology and business point of view. Experience design can help focus projects on the right sector to pursue based on user needs or user experience. Furthermore, experience design provides the tangible and visual support needed to facilitate the expansion and propagation of the original idea whilst often generating valuable intellectual property.
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To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimization problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimization to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments. More details on our research can be found in the Explore section below.
For a robot to be useful it must be able to represent its knowledge of the world, share what it learns and interact with other agents, in particular humans. Our research combines expertise in human-robot interaction, natural language processing, speech, information retrieval, data management and low code/no code programming to build AI components that will help next-generation robots perform complex real-world tasks. These components will help robots interact safely with humans and their physical environment, other robots and systems, represent and update their world knowledge and share it with the rest of the fleet. More details on our research can be found in the Explore section below.
Visual perception is a necessary part of any intelligent system that is meant to interact with the world. Robots need to perceive the structure, the objects, and people in their environment to better understand the world and perform the tasks they are assigned. Our research combines expertise in visual representation learning, self-supervised learning and human behaviour understanding to build AI components that help robots understand and navigate in their 3D environment, detect and interact with surrounding objects and people and continuously adapt themselves when deployed in new environments. More details on our research can be found in the Explore section below.
Details on the gender equality index score 2024 (related to year 2023) for NAVER France of 87/100.
The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and Indicator n°4: +5 points in 2025) have been achieved.
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2024 au titre des données 2023 : 87/100
Détail des indicateurs :
Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2 points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.
Details on the gender equality index score 2024 (related to year 2023) for NAVER France of 87/100.
1. Difference in female/male salary: 34/40 points
2. Difference in salary increases female/male: 35/35 points
3. Salary increases upon return from maternity leave: Non calculable
4. Number of employees in under-represented gender in 10 highest salaries: 5/10 points
The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and Indicator n°4: +5 points in 2025) have been achieved.
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2024 au titre des données 2023 : 87/100
Détail des indicateurs :
1. Les écarts de salaire entre les femmes et les hommes: 34 sur 40 points
2. Les écarts des augmentations individuelles entre les femmes et les hommes : 35 sur 35 points
3. Toutes les salariées augmentées revenant de congé maternité : Incalculable
4. Le nombre de salarié du sexe sous-représenté parmi les 10 plus hautes rémunérations : 5 sur 10 points
Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2 points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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