Article written at the occasion of the XRCE 20th anniversary celebration.
The creation of a research centre in Europe in 1993 was warmly welcomed by Xerox sales in Europe, which had a burgeoning interest in new technology and desire to meld the reputation of Xerox as an innovator with customers’ needs.This invaluable link between research and sales has had a great influence on the centre’s growth and success.
Created to drive the corporate transition in becoming a services-led technology business, the centre’s focus has always been on targeting innovative software solutions and services. The customer’s role in this mission is reflected in the centre’s “work practices” research competency which is based on ethnography applied to the work environment, and its Technology Showroom, an innovative effort at the very heart of the centre where researchers demonstrate their findings to Xerox customers and collect feedback.
Research and the Customer: Some Lessons Learned
Lesson 1: Customer Show and Tell
When XRCE opened, every European country was considered as an independent business unit. This organizational structure enabled ownership and the creation of a network of account managers that would directly link customers with research efforts by coordinating and promoting visits to the centre’s Technology Showroom. Some of the early visitors were surprised by the technologies they saw. Software development seemed very different from research related to copiers or production printers. However customers became excited by the idea of more services provided by Xerox. In a business world dominated at this time by paper-based information, Xerox was using its expertise in documents to boost business productivity. As account managers brought the first stream of visitors from all over Europe to the showroom, researchers gained insight into the topics that would interest customers.
At this point, the Technology Showroom was mainly used as a sales and promotion tool that would contribute to enhancing the customer relationship. As the European research centre better understood the value of directly involving customers in its research activities, it began to devise a recipe for getting the most out of customer meetings. Sales teams helped identify customers who were willing to innovate, had a real problem to solve and would support the strategic value of the centre’s innovation efforts by following the requirements of an experiment and deploying the solution.
Lesson 2: Creating good demonstrations, telling good stories and listening to the customer
One key challenge for researchers is communicating the value of a technology to customers. While not everyone is a Steve Jobs, a good demonstration and polished storytelling skills go a long way to helping create excitement and interest. For example, a researcher working on classification technologies might take the time to share the difficulty he or she had with managing personal holiday pictures. It’s also important to learn as much as possible about the customer, and adapt the demonstration to a customer’s specific work process. Customizing a demonstration by simply adding the logo of the company you are presenting to is another simple way to capture a customer’s attention. This approach is very connected to the notion of Minimum Viable Product advocated by entrepreneur and author Eric Ries which says that as a researcher learns more from his demonstrations, he incorporates new features, leading to a demonstration that is more convincing to investors.
Lesson 3: Establish a methodology and find a name for it
When I discussed with Paul Millier, professor of Markets and Innovation at the Lyon business school (EM Lyon) our effort to involve customers in our innovation process, he encouraged me to continue this path and immediately adhered to the name of ‘Dreaming Sessions’ that we had chosen for our efforts. This was an expression we had coined with Sophie Vandebroek, Xerox CTO, to describe our customer engagement sessions.
Methodology is equally important as it enables a wider use of practices across an organization. Our process starts with the Customer Led Innovation team receiving requests both from the sales organization and researchers to organize a “dreaming session” with customers. The team is in charge of connecting the dots: selecting the right technologies to discuss with the customer depending of their industry, their role and what the researchers would like to learn.
The second stage is a proposal describing the dreaming session which includes an agenda and description of expectations. The third stage is the preparation of the dreaming session: demonstrations have to be prepared with an increased focus on customization to the customer needs. The Customer Led Innovation Team works with researchers to decide on a list of questions that will be answered by customers.
The third stage is the dreaming session itself. It happens when all stakeholders agree on the content. During a session, the Customer Led Innovation team is in charge of creating rich interactions between customers and researchers. Scenarios are established for using all presented technologies. At the end of the dreaming session, customers are asked to rank the relative importance of the different technologies. This information is used in order to prioritize which technologies could be pursued with pilots. As part of this stage, notes are captured and sometimes video can record the interactions.
The fourth stage consists in sharing the knowledge acquired during the Dreaming Session: this information can be critical in order to guide the different applications of a technology.
The fifth stage consists in engaging the customer in a pilot, where the technology would be deployed. The opportunity for pilots is the best way for researchers and Xerox to prove the value of a technology and involve the lines of business that will be delivering the new technology or service on a wider scale.
Lesson 4: It takes teamwork
There are two aspects of Dreaming Sessions that are critical: listening and relationship building. Involving researchers has many advantages. It gives them the opportunity to listen and collect feedback. They are skilled and trained in demonstrating the technologies, and the sessions help illustrate the business value proposition of a technology. However, there needs to be a team in charge of facilitating the relationship with the customer during the demonstration.
Lesson 5: decide on objectives and metrics to improve the activity
As with any activity at Xerox, Dreaming Sessions have objectives. When the sessions first began, it was not always easy to find the right balance of objectives. From the innovator’s point of view, what should ideally be measured would be the number of ideas, comments, scenarios received when discussing a technology with customers. From a customer relationship standpoint, it would lean toward the additional revenue generated after having invited customers to a dreaming session. Both areas are very difficult to track, so we decided to keep is it fairly simple by setting objectives associated with the number of dreaming sessions organized per year, the number of researchers involved in dreaming sessions and tracking globally the revenue the dreaming sessions were associated with and lastly the number of new technologies on display. Setting up objectives against these goals met the needs of all stakeholders.
Lesson 6: do not mess around with intellectual property
Over the years, dreaming sessions have been a point of debate with our lawyer friends because they can potentially generate new ideas. Patent attorneys become nervous when we start to talk of co-ownership. Some rules that we have tried to follow over the years:
It’s important to note that things are slightly different if technical intellectual property has to be disclosed. In these cases we’ve worked with thorough and creative lawyers who helped us set the right context for these sessions.
The future of Dreaming Sessions
What will be the future of dreaming sessions? Things have dramatically changed over these past 20 years. I remember one of my first customer events with a European University. We tried to pile as many technologies as possible in a day, without understanding the whole context and expectations of the customer. The outcome was not very successful, leading to the customer falling asleep after lunch!
Today we recognized that a lot can be learned when technologies are put in the hands of a customer at a very early stage. New scenarios of applications can pop up, and preconceived ideas can be put aside without spending too much energy on them. Additionally, new modalities have emerged enabled by the digital age. Xerox Research Centre Europe recently launched “Open Xerox”, a test bed where new technologies capable of working on a Web base can be demonstrated and tested. Many of these can be considered individually as Minimum Viable Products, but what about using FaceBook to discuss a technology? This may be possible in the future, but many questions have to be answered. We would have to look for ways to protect the Xerox brand. Competitors, for example, may access the technology and influence its development. Confidentiality might also be difficult to manage. But what an avenue to reach millions of future users!
The work done in the European research lab has certainly influenced the emergence of Living Labs, an open-innovation ecosystem where researchers, future users of the technology and various stakeholders can experiment with new technologies. The future will certainly be more opened, participative and will lead to better research finding faster applications because designed with its users, using the best interaction to do so.
NAVER LABS Europe 6-8 chemin de Maupertuis 38240 Meylan France Contact
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.
—————
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.
——————-
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.
This web site uses cookies for the site search, to display videos and for aggregate site analytics.
Learn more about these cookies in our privacy notice.
You may choose which kind of cookies you allow when visiting this website. Click on "Save cookie settings" to apply your choice.
FunctionalThis website uses functional cookies which are required for the search function to work and to apply for jobs and internships.
AnalyticalOur website uses analytical cookies to make it possible to analyse our website and optimize its usability.
Social mediaOur website places social media cookies to show YouTube and Vimeo videos. Cookies placed by these sites may track your personal data.
This content is currently blocked. To view the content please either 'Accept social media cookies' or 'Accept all cookies'.
For more information on cookies see our privacy notice.