Internship: learning human aware robot navigation F/M

About NAVER LABS Europe

NAVER LABS Europe is part of the R&D division of NAVER, Korea’s leading Internet portal and a global tech company with a range of services that include search, commerce, content, fintech, robotics and cloud.

The position

At Naver Labs we are working on the next generation of robot navigation approaches, and we target this with end-to-end models trained for quick and precise motion with realistic (identified) motion models (See our research topic post) and CVPR 2025 paper [JPA+2025]. Our agents are large-scale trained, and also include pre-trained geometric foundation models [BAC+2023][MRD+2025].

Figure 1: A Naver rookie robot at Naver Labs Europe navigating in a real environment.

Our current models are capable of navigating on real robots, in particular our Naver Rookie Around platform. They navigate very quickly, precisly and are capable of avoiding obstacles, including humans. However, they are not trained to actively understand human motion, and they certainly do not anticipate future positions, or social norms.

This internship will focus on learning robotic policies which are efficient in crowded scenarios, and respect social norms. We target data-driven models learning human behavior from web-scale data, integrating learned models into RL-trained policies.

About the research team

In the Action group, we develop AI-driven decision-making capabilities that enable embodied agents to safely execute complex tasks in dynamic environments. To achieve autonomy in real-world everyday spaces, robots must learn from their interactions, understand how to best execute tasks specified by non-expert users, and do so in a safe and reliable manner. This requires sequential decision-making skills that integrate machine learning, adaptive planning, and control in uncertain environments, as well as the ability to solve hard combinatorial optimization problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim-to-real transfer, large multimodal foundation models, and neural combinatorial optimization to design AI-based architectures and algorithms that enhance robot autonomy and robustness when completing complex tasks in constantly changing environments.

What we're looking for (depending on your career stage)

The ideal candidate is rigorous and creative, has good coding skills and familiarity with deep learning and robotics. You will join a team of people working on the topic and have access to mobile robot platforms and computation resources to experiment with new ideas.

Profile:

• Last year MSc or PhD student in Computer Vision, Computer Science or close domains, existing publications in top conferences will provide a significant advantage in the application process.
• Solid background in deep learning
• Excellent coding skills, especially in PyTorch
• Familiarity with ROS and robotics are a plus
• Knowledge of deep reinforcement learning and SLAM are a plus​

What we offer

• We foster a collaborative environment dedicated to ambitious, multidisciplinary projects that translate advanced research into impactful, real-world solutions, supported by 30+ years of experience in AI and related fields.

• Flexible work/life balance

• We are an equal opportunity employer that hires based on skills, experience, and merit. We foster an inclusive and diverse workplace where all qualified candidates are considered fairly, regardless of background.

• We’re based in Meylan, close to Grenoble, a city that offers the perfect balance of urban life, cutting-edge research and technology, and spectacular mountain landscapes that provide countless opportunities to relax, recharge, and enjoy the outdoors.

All applications will be carefully considered, even if not all required skills are met. We value diverse backgrounds and the potential of each candidate, and we offer training to support the development of necessary skills.

NAVER LABS, co-located in Korea and France, is the organization dedicated to preparing NAVER’s future. 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 ways to achieve these goals. Teamwork, focus and persistence are important values for us.

 When applying for this position online, please don't forget to upload your CV and cover letter. Incomplete applications will not be considered.

NAVER LABS Europe is subject to French jurisdiction requiring organisations to stipulate that a job/internship is open to both women and men. None of our jobs/internships are gender specific.

References

• [BAC+2023] Guillaume Bono, Leonid Antsfeld, Boris Chidlovskii, Philippe Weinzaepfel and Christian Wolf. End-to-End (Instance)-Image Goal Navigation through Correspondence as an Emergent Phenomenon, In International Conference on Learning Representations (ICLR), 2024.
  
• [BAC+-2023b] Guillaume Bono, Leonid Antsfeld, Assem Sadek, Gianluca Monaci and Christian Wolf. Learning with a Mole: Transferable latent spatial representations for navigation without reconstruction, In International Conference on Learning Representations (ICLR), 2024.
  
  
• [BPA+2024] Guillaume Bono, Hervé Poirier, Leonid Antsfeld, Gianluca Monaci, Boris Chidlovskii, and Christian Wolf. Learning to navigate efficiently and precisely in real environments. In International Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
  
  
• [DSM+2023] Sombit Dey, Assem Sadek, Gianluca Monaci, Boris Chidlovskii and Christian Wolf. Learning whom to trust in navigation: dynamically switching between classical and neural planning. In International Conference on Intelligent Robots and Systems (IROS), 2023.
  
  
• [JPA+2025] Steeven Janny, Hervé Poirier, Leonid Antsfeld, Guillaume Bono, Gianluca Monaci, Boris Chidlovskii, Francesco Giuliari, Alessio Del Blue, and Christian Wolf. Reasoning in visual navigation of end-to-end trained agents: a dynamical systems approach. In International Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
  
  
• [MMS+2023] Pierre Marza, Laetitia Matignon, Olivier Simonin and Christian Wolf. Multi-Object Navigation with dynamically learned neural implicit representations. In International Conference on Computer Vision (ICCV), 2023.
  
  
• [MRD+2025] Gianluca Monaci, Rafael S. Rezende, Romain Deffayet, Gabriela Csurka, Guillaume Bono, Hervé Déjean, Stéphane Clinchant, and Christian Wolf. RANa: Retrieval-Augmented Navigation. arxiv:2504.03524, 2025.
  
  
• [SBC+2023] Assem Sadek, Guillaume Bono, Boris Chidlovskii, Atilla Baskurt, and Christian Wolf. Multi-Object Navigation in real environments using hybrid policies. In International Conference on Robotics and Automation (ICRA), 2023.
  
  
• [SBC+2022] Assem Sadek, Guillaume Bono, Boris Chidlovskii and Christian Wolf. An in-depth experimental study of sensor usage and visual reasoning of robots navigating in real environments. In ICRA, 2022.

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