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1st “AI for Robotics” Workshop at NAVER LABS Europe
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Two day workshop with keynotes, demonstrations and a poster session.
How AI can help integrate robots into everyday life. International workshop with leading experts from academia and industry.
Dates: Thursday November 28th and Friday November 29th
Location: NAVER LABS Europe, 6 Chemin de Maupertuis, 38240 Meylan (Grenoble, France). By invitation only.
Remarkable results in computer vision, reinforcement learning, scene understanding and related fields have led to being able to equip robots with AI components so they can operate in the real world. This is a significant shift from the highly-controlled environments they had previously been restricted to, as well as the very clearly defined tasks they were able to undertake in these environments. In the not-too-distant future, we expect that further advances in AI will integrate robots and our interaction with them, into our everyday lives.
In this workshop, we address the question of how AI can help to solve the biggest challenges of real-world robotics applications such as understanding and navigating complex dynamic environments, interacting with humans and learning to accomplish tasks autonomously. The event brings together researchers and experts from different AI and Robotics disciplines to discuss current and future directions of these fields in an informal setting to facilitate the creation of connections and collaborations.
Poster session:
In order to make the workshop more interactive and to give more people the opportunity to actively contribute to the workshop, we will organize a poster session for, e.g., PhD students (or anybody else) to present and discuss their work. For this, we will provide small travel grants of 300 Euro on a first come first serve basis.
Workshop Programme
| Thursday, 28th November | |
|---|---|
| 09:30 | Registration |
| 10:00 | Welcome |
| 10:15 | Marc Pollefeys, ETH Zürich: Mixed Reality and Robotics |
| 11:00 | Christian Wolf, INSA & CNRS: Integrating Learning and Projective Geometry for Robotics |
| 12:00 | Lunch |
| 13:30 | Sangok Seok, NAVER LABS: New Connections Between People, Spaces and Information: Robotics, Autonomous Driving, AI and 5G |
| 14:15 | Sangbae Kim, MIT: Robots with Physical Intelligence |
| 15:00 | Break |
| 15:30 | Vincent Lepetit, ENPC: 3D Scene Understanding from a Single Image |
| 16:15 | Torsten Sattler, Chalmers University of Technology: Visual Localization: To Learn or Not to Learn? |
| 17:00 | Horst Bischof, Graz University of Technology: Understanding long-term complex activities |
| 17:45 | End of day 1 |
| 19:15 | Social event |
| Friday, 29th November | |
|---|---|
| 09:00 | Welcome |
| 09:15 | Daniel Cremers, Technische Universität München: Direct Visual SLAM for Autonomous Systems |
| 10:00 | Alexandre Alahi, EPFL: Socially-aware AI for Last-mile Mobility |
| 10:45 | Martin Humenberger, NAVER LABS Europe: New Approaches in Robot Perception |
| 11:30 | Lunch |
| 13:00 | Radu Horaud, INRIA: Audio-visual Machine Perception for Socially Interacting Robots |
| 13:45 | Poster and Demo Session |
| 15:00 | Cordelia Schmid, INRIA & Google: Learning to Combine Primitive Skills: A Versatile Approach to Robotic Manipulation |
| 15:45 | Closing |
| 16:00 | End of day 2 |
About the organizers:
NAVER LABS Europe is the biggest industrial AI research center in France and the sister lab, NAVER LABS (Korea), is a leading robotics research organization with robots like the M1X mapping robot, the service platform AROUND, and the 5G connected robot arm AMBIDEX. Both organizations are owned by NAVER Corporation, Korea’s leading internet company and 9th on Forbes 2018 list of the world’s most innovative companies.
Topics include:
- Solving real-world challenges of robotic applications using AI
- Operating robots in ever-changing environments
- AI to improve the interaction between robots and humans
- How robots can learn from humans to accomplish tasks
Related Content
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.
VISION
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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