13 accepted papers
In high-density environments where numerous autonomous agents move simultaneously in a distributed manner, streamlining global flows to mitigate local congestion is crucial to maintain overall navigation efficiency. This paper introduces a novel path-planning problem, <italic xmlns:mml="http://www.w
Search and rescue (SAR) operations in large-scale disaster sites, such as areas affected by earthquakes, require rapid victim detection. While drones equipped with cameras are commonly used for SAR, their effectiveness is limited in visually obstructed environments, because of debris, smoke, or fog.
This paper presents a reinforcement learning method for object goal navigation (ObjNav) where an agent navigates in 3D indoor environments to reach a target object based on long-term observations of objects and scenes. To this end, we propose Object Memory Transformer (OMT) that consists of two key…
Extensive city navigation remains an unresolved problem for autonomous mobile robots that share space with pedestrians. This paper proposes a configuration for a navigation map that expresses urban structures and an autonomous navigation scheme that uses the configuration. The proposed map configura…
The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution. While goal conditioning of policies has been studied in the RL literature, such approaches are not easily extended to settings where the r
In this paper, we consider the problem of building learning agents that can efficiently learn to navigate in constrained environments. The main goal is to design agents that can efficiently learn to understand and generalize to different environments using high-dimensional inputs (a 2D map), while f…
Detecting sound source objects within visual observation is important for autonomous robots to comprehend surrounding environments. Since sounding objects have a large variety with different appearances in our living environments, labeling all sounding objects is impossible in practice. This calls f…
Following improvements in deep neural networks, state-of-the-art networks have been proposed for human recognition using point clouds captured by LiDAR. However, the performance of these networks strongly depends on the training data. An issue with collecting training data is labeling. Labeling by h…
Robot navigation using deep neural networks has been drawing a great deal of attention. Although reactive neural networks easily learn expert behaviors and are computationally efficient, they suffer from generalization of policies learned in specific environments. As such, reinforcement learning and
The paper proposes a spatial sound perception system for an autonomous mobile robot. The system performs three-dimensional position localization and recognition as online processing from a robot in motion. For online processing, the sound positions are estimated as probabilistic regions in three dim…
The paper proposes a system for mapping the 3D location of a sound source using data from a microphone array, each of which gives an independent estimate of the direction. LiDAR is used to generate a 3D map of the environment and to estimate the location of the sensor in six degrees of freedom (6-Do…
Analyzing the auditory scene of real environments is challenging partly because an unknown number and type of sound sources are observed at the same time and partly because these sounds are observed on a significantly different sound pressure level at the microphone. These are difficult problems eve…