Mobile manipulation is a family of tasks involving the manipulation of objects by articulated robot arms mounted on mobile plateforms, and involving a joint action space including repositioning of the plateform and manipulation actions. Mobile manipulators have been investigated for decades [1] [2] [3], because of their usefulness for servicing many of the everyday tasks for human beings. However, most of the mobile manipulators that actually operated in everyday life are generally teleoperated [4]. Fully autonomous mobile-manipulations through deep learning also have been studied [5][6]. However, these approaches simplify the manipulation parts, by considering the simple task of grasping objects on the floor, and by simplifying the actions as discrete single-step top-down action [6] [5].
Designing and training a robot to deal with long-horizon mobile-manipulation tasks remains a challenge, in particular if the robot has to manipulate multiple and diverse objects in a real environment. Recent work in the NAVER LABS Spatial AI team, the Option-Transformer, deals with solving such long-horizon tasks. The approach provides a way for discovering a set of reusable skills from previous experiences so that the skills can be used for diverse, previously unseen down-stream tasks. The approach was validated with manipulation and navigation tasks separately.
The goal of the internship is to extend the Option-Transformer architecture for mobile-manipulation tasks. The candidate will carry out research and development activities to achieve important milestones toward this goal:
The successful candidate should be enrolled in a graduate program, at the Master or PhD level.
- Ability to propose and implement research ideas, evaluate them empirically, analyze them deeply and iterate on them.
- Ability to perform collaborative research and work in a team.
- Strong competencies with scientific computing platforms and programming languages such as PyTorch and Tensorflow.
- Relevant experience in machine learning
- Experience in robotics is a plus
[1] A. Petrovskaya and A. Y. Ng, “Probabilistic mobile manipulation in dynamic environments, with ap- plication to opening doors,” in IJCAI, 2007.
[2] M. Giftthaler, F. Farshidian, T. Sandy, L. Stadelmann, and J. Buchli, “Efficient Kinematic Plan- ning for Mobile Manipulators with Non-holonomic Constraints Using Optimal Control,” in IEEE International Conference on Robotics and Automation, pp. 3411–3417, 2017. arXiv: 1701.08051.
[3] J. Pankert and M. Hutter, “Perceptive Model Predictive Control for Continuous Mobile Manipula- tion,” IEEE Robotics and Automation Letters, vol. 5, pp. 6177–6184, 2020.
[4] J. Wong, A. Tung, A. Kurenkov, A. Mandlekar, L. Fei-Fei, S. Savarese, and R. Mart ́ın-Mart ́ın, “Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation,” in Conference on Robot Learning, 2022.
[5] A. Gupta, A. Murali, D. P. Gandhi, and L. Pinto, “Robot Learning in Homes: Improving General- ization and Reducing Dataset Bias,” NeurIPS, 2018.
[6] C. Sun, J. Orbik, C. Devin, B. Yang, A. Gupta, G. Berseth, and S. Levine, “Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation,” arXiv:2107.13545 [cs], Dec. 2021. arXiv: 2107.13545.
Please note that applicants must be registered students at a university or other academic institution and that this establishment will need to sign an 'Internship Convention' with NAVER LABS Europe before the student is accepted.
You can apply for this position online. Don't forget to upload your CV and cover letter before you submit. Incomplete applications will not be accepted.
NAVER is the #1 Internet portal in Korea with activities that span a wide range of businesses including search, commerce, content, financial and cloud platforms.
NAVER LABS, co-located in Korea and France, is the organization dedicated to preparing NAVER’s future. NAVER LABS Europe is located in a spectacular setting in Grenoble, in the heart of the French Alps. Scientists at NAVER LABS Europe are empowered to pursue long-term research problems that, if successful, can have significant impact and transform NAVER. We take our ideas as far as research can to create the best technology of its kind. Active participation in the academic community and collaborations with world-class public research groups are, among others, important tools to achieve these goals. Teamwork, focus and persistence are important values for us.
NAVER LABS Europe is an equal opportunity employer.
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.
NAVER LABS Europe 6-8 chemin de Maupertuis 38240 Meylan France Contact
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 robot understand and navigate in their 3D environment, detect and interact with surrounding objects and people and continuously adapt themselves when deployed in new environments.
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.
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.
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.