Methods representing space suitable to learning, semantic concept and the relation to human cognitive capabilities (original) (raw)

Towards a cognitive probabilistic representation of space for mobile robots

2006

Robots are rapidly evolving from factory workhorses to robot-companions. The future of robots, as our companions, is highly dependent on their abilities to understand, interpret and represent the environment in an efficient and consistent fashion, in a way that is comprehensible to humans. This paper is oriented in this direction. It suggests a hierarchical probabilistic representation of space that is based on objects. A global topological representation of places with object graphs serving as local maps is suggested. Experiments on place classification and place recognition are also reported in order to demonstrate the applicability of such a representation in the context of understanding space and thereby performing spatial cognition. Thus the theme of the work is -representation for spatial cognition.

Cognitive spatial representations for mobile robots-perspectives from a user study

IEEE Int. Conf. on Robotics and …, 2007

Robots are rapidly evolving from factory work-horses to robot-companions. The future of robots, as our companions, is highly dependent on their ability to understand, interpret and represent the environment in an efficient and consistent fashion, in a way that is comprehensible to humans. The work presented here is oriented in this direction. It suggests a hierarchical concept oriented representation of space that is based on objects. This work attempts to provide a "cognitive" validation to the proposed representation and also looks into ways of enhancing it. This is done by means of an elaborate user study experiment. Analysis of the data obtained from the user study provides a human perspective to the robotics problem. This work also attempts to put forward a more generic methodology in order to develop such a representation, to be able to map the robots sensory information to increasingly abstract concepts that describe the semantics of the space the robot inhabits. The work itself is aimed at radically improving the degree of spatial awareness of state-of-the-art robot systems. Thus, the theme of the work is -representation for spatial cognition.

Towards a multilevel cognitive probabilistic representation of space

Human Vision and Electronic Imaging X, 2005

This paper addresses the problem of perception and representation of space for a mobile agent. A probabilistic hierarchical framework is suggested as a solution to this problem. The method proposed is a combination of probabilistic belief with "Object Graph Models" (OGM). The world is viewed from a topological optic, in terms of objects and relationships between them. The hierarchical representation that we propose permits an efficient and reliable modeling of the information that the mobile agent would perceive from its environment. The integration of both navigational and interactional capabilities through efficient representation is also addressed. Experiments on a set of images taken from the real world that validate the approach are reported. This framework draws on the general understanding of human cognition and perception and contributes towards the overall efforts to build cognitive robot companions.

From Sensors to Human Spatial Concepts: An Annotated Data Set

IEEE Transactions on Robotics, 2000

An annotated data set is presented meant to help researchers in developing, evaluating and comparing various approaches in robotics for building space representations appropriate for communicating with humans. The data consists of omnidirectional images, laser range scans, sonar readings and robot odometry. A set of base-level human spatial concepts is used to annotate the data.

Cognitive maps for mobile robots - an object based approach

Robotics and Autonomous …, 2007

Robots are rapidly evolving from factory work-horses to robot-companions. The future of robots, as our companions, is highly dependent on their abilities to understand, interpret and represent the environment in an efficient and consistent fashion, in a way that is comprehensible to humans. The work presented here is oriented in this direction. It suggests a hierarchical probabilistic representation of space that is based on objects. A global topological representation of places with object graphs serving as local maps is proposed. The work also details the first efforts towards conceptualizing space on the basis of the human compatible representation so formed. Such a representation and the resulting conceptualization would be useful for enabling robots to be cognizant of their surroundings. Experiments on place classification and place recognition are reported in order to demonstrate the applicability of such a representation towards understanding space and thereby performing spatial cognition. Further, relevant results from user studies validating the proposed representation are also reported. Thus, the theme of the work is -representation for spatial cognition.

Graph-based models of space in architecture and cognitive science-a comparative analysis

2005

Graph-based operationalizations of space are used in architecture as well as in cognitive science. In such models, environments are usually described by means of nodes and edges, roughly corresponding to places and their spatial relations. In the field of cognitive science, view and place graphs are models of mental representations of environments and used for the explanation of wayfinding behavior such as exploration and route planning. In architecture, space syntax and visibility graph analysis aim at identifying and describing structural properties of built environments that determine their usage and experience. In cognitive science, mental representations of space cannot be seen independently from the formal and configurational properties of the corresponding environments that are well captured by architectural description systems. Vice versa, formal descriptions of space as used in architecture gain plausibility and relevance by incorporating results from cognitive research that allow the prediction and explanation of actual human behavior. In this paper approaches from the two different disciplines are therefore reviewed and compared. Special interest concerns their scope, structure, and representational content. Parallels, differences, and specific strengths are discussed. Furthermore, based on recent empiric work, strategies to integrate aspects from both disciplines are outlined.

The spatial semantic hierarchy

2000

The Spatial Semantic Hierarchy is a model of knowledge of large-scale space consisting of multiple interacting representations, both qualitative and quantitative. The SSH is inspired by the properties of the human cognitive map, and is intended to serve both as a model of the human cognitive map and as a method for robot exploration and map-building. The multiple levels of the SSH express states of partial knowledge, and thus enable the human or robotic agent to deal robustly with uncertainty during both learning and problem-solving.

Mapping Space: A Comparative Study

2019

The semantics of spatial terms has attracted substantial attention in the cognitive sciences, revealing both compelling similarities and striking differences across languages. However, much of the evidence regarding cross-linguistic variation pertains to fine-grained comparisons between individual lexical items, while cross-linguistic similarities are found in more coarse-grained studies of the conceptual space underlying semantic systems. We seek to bridge this gap, moving beyond the semantics of individual terms to ask what the comparison of spatial semantic systems may reveal about the conceptualization of locations in English and Mandarin Chinese and about the nature of potential universals in this domain. We subjected descriptions of 116 spatial scenes to multidimensional scaling analyses in order to reveal the structures of the underlying conceptual spaces in each language. In addition to revealing overlaps and divergences in the conceptualization of space in English and Manda...

A Hierarchical Concept Oriented Representation for Spatial Cognition in Mobile Robots

2006

Robots are rapidly evolving from factory work-horses to robot-companions. The future of robots, as our companions, is highly dependent on their abilities to understand, interpret and represent the environment in an efficient and consistent fashion, in a way that is compatible to humans. The work presented here is oriented in this direction. It suggests a hierarchical, concept oriented, probabilistic representation of space for mobile robots. A salient aspect of the proposed approach is that it is holistic - it attempts to create a consistent link from the sensory information the robot acquires to the human-compatible spatial concepts that the robot subsequently forms, while taking into account both uncertainty and incompleteness of perceived information. The approach is aimed at increasing spatial awareness in robots.