29 September 2021
A novel approach to indoor localization that uses magnetic field data from smartphone sensors and deep learning
2021
30 August 2021
A new approach to learning few-shot imitation agents whereby you simply feed demonstrations of a new test task to the learned policy called DCRL. This new approach has several advantages.
2021
15 July 2021
A new approach to learning few-shot imitation agents whereby you simply feed demonstrations of a new test task to the learned policy called DCRL. This new approach has several advantages.
2021
19 May 2021
NAVER LABS releases world's biggest visual localization dataset of indoor spaces with over 130K images. Dataset built with NAVER LABS mapping robots M1X & COMET and available in unified data format kapture.
2021
14 May 2021
Using a novel algorithm we explore how effectively a single policy, learned by reinforcement learning, can modulate robot behaviour from risk-averse to risk-neutral, so that robots can safely navigate everyday environments like homes and shops.
2021
31 March 2021
Visual localization is used by robots and self-driving cars to estimate their position and in AR apps to interact with the physical world both indoors and out in the open. This article gives an overview of current state-of-the-art methods and their advantages and drawbacks.
2021
10 March 2021
Podcast and transcript of Matthias Gallé, head of the NAVER research LAB in Europe who tells us what kind of research is going on in the labs in France and what it’s like to work there.
2021
27 February 2021
Agents are becoming more artificially intelligent but a number of problems must be solved before we can unleash them into our physical world (i.e. robots) and trust them in our digital one.
2021
7 December 2020
Podcast and transcript on 3rd Robot Learning Workshop at NeurIPS 2020 with Julien Perez, group lead machine learning and optimization at NAVER LABS Europe and co-organiser of the workshop.
2020
21 August 2020
A novel efficient model for whole-body 3D pose estimation (including bodies, hands and faces), that is trained by mimicking the output of hand-, body- and face-pose experts.
2020
12 June 2020
A first-of-its-kind architecture that, based on a single image, predicts how a robot can pick up objects from within any scene could revolutionize applications in AR/VR and robotics.
2020
28 May 2020
Our navigation system enables robots to adapt to specific real-world environments and use cases with only small amounts of human preference data.
2020