Abstract
Assem Sadek, Guillaume Bono, Boris Chidlovskii, Christian Wolf |
IEEE International Conference on Robotics and Automation (ICRA), Philadelphia, USA,23-27 May, 2022. |
arXiv |
Download |
Abstract
Visual navigation by mobile robots is classically tackled through SLAM plus optimal planning, and more recently through end-to-end training of policies implemented as deep networks. While the former are often limited to waypoint planning, but have proven their efficiency even on real physical environments, the latter solutions are most frequently employed in simulation, but have been shown to be able learn more complex visual reasoning, involving complex semantical regularities. Navigation by real robots in physical environments is still an open problem. End-to-end training approaches have been thoroughly tested in simulation only, with experiments involving real robots being restricted to rare performance evaluations in simplified laboratory conditions. In this work we present an in-depth study of the performance and reasoning capacities of real physical agents, trained in simulation and deployed to two different physical environments. Beyond benchmarking, we provide insights into the generalization capabilities of different agents training in different conditions. We visualize sensor usage and the importance of the different types of signals. We show, that for the PointGoal task, an agent pre-trained on wide variety of tasks and fine-tuned on a simulated version of the target environment can reach competitive performance without modelling any sim2real transfer, i.e. by deploying the trained agent directly from simulation to a real physical robot.
Details on the gender equality index score 2023 (related to year 2022) for NAVER France of 81/100.
NAVER France targets are as follows:
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2023 au titre des données 2022 : 81/100
Détail des indicateurs :
Les objectifs de progression de NAVER France sont :
NAVER LABS Europe 6-8 chemin de Maupertuis 38240 Meylan France Contact
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