LexToMap: lexical-based topological mapping

Rangel, José Carlos; Martínez‐Gómez, Jesús; García-Varea, Ismael; Cazorla, Miguel

doi:10.1080/01691864.2016.1261045

Cited by 10 publications

(6 citation statements)

References 35 publications

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“…Topp [139] addresses the issue of learning from a human teacher in situated communications, where the robot needs to detect inconsistencies in the information provided by the human teacher. In the work put forward by Rangel et al [140], this information is not learned from dialogue, but instead learned from manually annotated images. These annotations are used to learn a spatial representation of the environment from the visual processing of raw images.…”

Section: Discussionmentioning

confidence: 99%

A review of spatial reasoning and interaction for real-world robotics

et al. 2017

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Truly universal helper robots capable of coping with unknown, unstructured environments must be capable of spatial reasoning, i.e., establishing geometric relations between objects and locations, expressing those in terms understandable by humans. It is therefore desirable that spatial and semantic environment representations are tightly interlinked. 3D robotic mapping and the generation of consistent metric representations of space is highly useful for navigation and exploration, but they do not capture symbol-level information about the environment. This is, however, essential for reasoning, and enables interaction via natural language, which is arguably the most common and natural communication channel used and understood by humans.This article presents a review of research in three major fields relevant for this discussion of spatial reasoning and interaction. Firstly, dialogue systems are an integral part of modern approaches to situated human-robot interaction. Secondly, interactive robots must be equipped with environment representations and reasoning methods that is suitable for both navigation and task fulfillment, as well as for interaction with human partners. Thirdly, at the interface between these domains are systems that ground language in systemic environment representation and which allow the integration of information from natural language descriptions into robotic maps. For each of these areas, important approaches are outlined and relations between the fields are highlighted, and challenging applications as well as open problems are discussed.

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Section: Discussionmentioning

confidence: 99%

A review of spatial reasoning and interaction for real-world robotics

et al. 2017

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“…A topological map consists of nodes that store information related to the location, such as vocabulary, images, and positions, and edges that represent transitions between the nodes. Rangel et al proposed LexToMap, which uses the CNN class of an object recognizer as the vocabulary label and generates a topological map in which the vocabulary label and the location node are related [8,41,46]. Balaska et al proposed a method of generating semantic maps based on a topological map using an unsupervised learning approach [10].…”

Section: A2 Semantic Mappingmentioning

confidence: 99%

“…' In the field of image recognition, several models have been proposed to estimate the image class of a place using convolutional neural networks (CNNs) [1][2][3][4]. In studies of semantic mapping, methods of assigning a place vocabulary or class to an occupied grid map [5][6][7] and methods using a topological map have been proposed [8][9][10]. These studies often use supervised learning techniques, such as CNNs, and require large-scale labeled datasets to learn model parameters.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Bayesian Model for the Transfer of Knowledge on Spatial Concepts based on Multimodal Information

Hagiwara¹,

Taguchi²,

Ishibushi³

et al. 2021

Preprint

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This paper proposes a hierarchical Bayesian model based on spatial concepts that enables a robot to transfer the knowledge of places from experienced environments to a new environment. The transfer of knowledge based on spatial concepts is modeled as the calculation process of the posterior distribution based on the observations obtained in each environment with the parameters of spatial concepts generalized to environments as prior knowledge. We conducted experiments to evaluate the generalization performance of spatial knowledge for general places such as kitchens and the adaptive performance of spatial knowledge for unique places such as 'Emma's room' in a new environment. In the experiments, the accuracies of the proposed method and conventional methods were compared in the prediction task of location names from an image and a position, and the prediction task of positions from a location name. The experimental results demonstrated that the proposed method has a higher prediction accuracy of location names and positions than the conventional method owing to the transfer of knowledge.

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“…Many studies have been conducted on spatial concept formation based on multimodal information observed in individual environments (Hagiwara et al, 2016 ; Heath et al, 2016 ; Rangel et al, 2017 ). Spatial concepts are formed in a bottom-up manner based on multimodal observed information, and allow predictions of different modalities.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Spatial Concept Formation Based on Multimodal Information for Human Support Robots

et al. 2018

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In this paper, we propose a hierarchical spatial concept formation method based on the Bayesian generative model with multimodal information e.g., vision, position and word information. Since humans have the ability to select an appropriate level of abstraction according to the situation and describe their position linguistically, e.g., “I am in my home” and “I am in front of the table,” a hierarchical structure of spatial concepts is necessary in order for human support robots to communicate smoothly with users. The proposed method enables a robot to form hierarchical spatial concepts by categorizing multimodal information using hierarchical multimodal latent Dirichlet allocation (hMLDA). Object recognition results using convolutional neural network (CNN), hierarchical k-means clustering result of self-position estimated by Monte Carlo localization (MCL), and a set of location names are used, respectively, as features in vision, position, and word information. Experiments in forming hierarchical spatial concepts and evaluating how the proposed method can predict unobserved location names and position categories are performed using a robot in the real world. Results verify that, relative to comparable baseline methods, the proposed method enables a robot to predict location names and position categories closer to predictions made by humans. As an application example of the proposed method in a home environment, a demonstration in which a human support robot moves to an instructed place based on human speech instructions is achieved based on the formed hierarchical spatial concept.

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LexToMap: lexical-based topological mapping

Cited by 10 publications

References 35 publications

A review of spatial reasoning and interaction for real-world robotics

A review of spatial reasoning and interaction for real-world robotics

Hierarchical Bayesian Model for the Transfer of Knowledge on Spatial Concepts based on Multimodal Information

Hierarchical Spatial Concept Formation Based on Multimodal Information for Human Support Robots

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