1 code implementation • 15 Sep 2021 • Angel Martínez-González, Michael Villamizar, Jean-Marc Odobez
We propose to leverage Transformer architectures for non-autoregressive human motion prediction.
1 code implementation • 2 Aug 2021 • Marco Ewerton, Angel Martínez-González, Jean-Marc Odobez
In this paper, we propose to frame the learning of pushing policies (where to push and how) by DQNs as an image-to-image translation problem and exploit an Hourglass-based architecture.
1 code implementation • 10 Nov 2020 • Angel Martínez-González, Michael Villamizar, Olivier Canévet, Jean-Marc Odobez
We propose to leverage recent advances in reliable 2D pose estimation with Convolutional Neural Networks (CNN) to estimate the 3D pose of people from depth images in multi-person Human-Robot Interaction (HRI) scenarios.
no code implementations • 2 Dec 2019 • Angel Martínez-González, Michael Villamizar, Olivier Canévet, Jean-Marc Odobez
i) we study several CNN architecture designs combining pose machines relying on the cascade of detectors concept with lightweight and efficient CNN structures; ii) to address the need for large training datasets with high variability, we rely on semi-synthetic data combining multi-person synthetic depth data with real sensor backgrounds; iii) we explore domain adaptation techniques to address the performance gap introduced by testing on real depth images; iv) to increase the accuracy of our fast lightweight CNN models, we investigate knowledge distillation at several architecture levels which effectively enhance performance.
no code implementations • 30 Oct 2019 • Angel Martínez-González, Michael Villamizar, Olivier Canévet, Jean-Marc Odobez
(i) we propose a fast and efficient network based on residual blocks (called RPM) for body landmark localization from depth images; (ii) we created a public dataset DIH comprising more than 170k synthetic images of human bodies with various shapes and viewpoints as well as real (annotated) data for evaluation; (iii) we show that our model trained on synthetic data from scratch can perform well on real data, obtaining similar results to larger models initialized with pre-trained networks.
no code implementations • 15 Sep 2019 • Mary Ellen Foster, Bart Craenen, Amol Deshmukh, Oliver Lemon, Emanuele Bastianelli, Christian Dondrup, Ioannis Papaioannou, Andrea Vanzo, Jean-Marc Odobez, Olivier Canévet, Yuanzhouhan Cao, Weipeng He, Angel Martínez-González, Petr Motlicek, Rémy Siegfried, Rachid Alami, Kathleen Belhassein, Guilhem Buisan, Aurélie Clodic, Amandine Mayima, Yoan Sallami, Guillaume Sarthou, Phani-Teja Singamaneni, Jules Waldhart, Alexandre Mazel, Maxime Caniot, Marketta Niemelä, Päivi Heikkilä, Hanna Lammi, Antti Tammela
In the EU-funded MuMMER project, we have developed a social robot designed to interact naturally and flexibly with users in public spaces such as a shopping mall.