Two papers from RLLAB are accepted to ICRA 2024

[2024.01.30]

Following papers are accepted to the IEEE International Conference on Robotics and Automation (ICRA 2024):

  • WayIL: Image-based Indoor Localization with Wayfinding Maps by Obin Kwon, Dongki Jung, Youngji Kim, Soohyun Ryu, Suyong Yeon, Songhwai Oh, and Donghwan Lee 

    • Abstract: This paper tackles a localization problem in large-scale indoor environments with wayfinding maps. A wayfinding map abstractly portrays the environment, and humans can localize themselves based on the map. However, when it comes to using it for robot localization, large geometrical discrepancies between the wayfinding map and the real world make it hard to use conventional localization methods. Our objective is to estimate a robot pose within a wayfinding map, utilizing RGB images from perspective cameras. We introduce two different imagination modules which are inspired by how humans can comprehend and interpret their surroundings for localization purposes. These modules jointly learn how to effectively observe the first-person-view (FPV) world to interpret bird-eye-view (BEV) maps. Providing explicit guidance to the two imagination modules significantly improves the precision of the localization system. We demonstrate the effectiveness of the proposed approach using real-world datasets, which are collected from various large-scale crowded indoor environments. The experimental results show that, in 85% of scenarios, the proposed localization system can estimate its pose within 3m in large indoor spaces.
    • Video

  • MAC-ID: Multi-Agent Reinforcement Learning with Local Coordination for Individual Diversity by Hojun Chung, Jeongwoo Oh, Jaeseok Heo, and Songhwai Oh

    • Abstract: With the increase of robots navigating through crowded environments in our daily lives, the demand for designing a socially-aware navigation method considering human-robot interaction has risen. When developing and assessing socially-aware navigation methods, pedestrian motion modeling plays a significant role. However, existing pedestrian models often struggle in complex environments and do not have the capacity to generate diverse pedestrian styles. In this paper, we propose multi-agent reinforcement learning with local coordination for individual diversity (MAC-ID), which can synthesize diverse pedestrian motions via local coordination factor (LCF). Our experiments have demonstrated that the manipulation of the LCF induces interpretable changes in pedestrian behaviors, along with superior performance compared to existing pedestrian motion models. For evaluating socially-aware navigation methods using MAC-ID, we present a novel benchmark called BSON. It offers realistic and diverse social environments with pedestrians modeled via MAC-ID. We have trained and compared various navigation methods in BSON using a newly proposed metric called socially-aware navigation score (SNS). Through BSON, users can evaluate their socially-aware navigation methods and compare them to baselines.
    • Video