Xerox Research Centre Europe Celebrates Two Decades of Innovation by Looking at the Future
Researchers open labs to the public on Oct. 4, offer world view of the future
GRENOBLE, France, Sept. 30, 2013 – What will the work place of the future look like? What will the adoption of self-driving cars lead to? What do economics and machine learning have to do with transportation? These and many other questions will be answered on Oct. 4 as Xerox celebrates 20 years of research at Xerox Research Centre Europe (XRCE). The center will be opening its doors for invited guests to attend technology demonstrations and talks by Xerox scientists and other distinguished speakers.
One of Xerox’s five global labs, XRCE is home to 60 researchers with expertise in areas such as ethnography (studying how people work); natural language processing, computer vision and data analytics. Over the years, the center has produced breakthrough technology to help automate document intensive business processes. Today, researchers are applying their expertise to solve new challenges in a wide range of industries including transportation, health care and customer care.
“We want to share the excitement we’ve been feeling in the lab as we take Xerox innovation beyond document devices to areas that directly support Xerox services and have a greater impact on society?,” said Monica Beltrametti, vice president and director Xerox Research Centre Europe. “It’s invigorating to apply our expertise in so many creative ways.”
Recent innovations from the center have helped transportation officials better manage parking and improve public transportation. In health care, research is aimed at bridging the gap between clinical research and clinical care, and in the telecommunications arena scientists are developing technology that helps companies provide better customer service in call centers.
The 20th birthday bash will include:
Video recordings of the talks and other content will be made available online, and can be found on the center’s website.
Nestled in the heart of the French Alps, XRCE is part of the global Xerox Innovation Group made up of 650 researchers and engineers in five world-renowned research centers
About Xerox
Since the invention of Xerography 75 years ago, the people of Xerox (NYSE: XRX) have helped businesses simplify the way work gets done. Today, we are the global leader in business process and document management, helping organizations of any size be more efficient so they can focus on their real business. Headquartered in Norwalk, Conn., more than 140,000 Xerox employees serve clients in 160 countries, providing business services, printing equipment and software for commercial and government organizations. Learn more at www.xerox.com.
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Media Contacts:
Irene Maxwell, Xerox Research Centre Europe, +33 4 76 61 50 83, irene.maxwell@xerox.com
Bill Mckee, Xerox, +1-585-423-4476, bill.mckee@xerox.com
Laurie Riedman, Riedman Communications for Xerox, +1-585-820-7617, laurie@riedmancomm.com
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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.
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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.
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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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