36,569 research outputs found
The attentive robot companion: learning spatial information from observation and verbal interaction
Ziegler L. The attentive robot companion: learning spatial information from observation and verbal interaction. Bielefeld: Universität Bielefeld; 2015.This doctoral thesis investigates how a robot companion can gain a certain degree of situational awareness through observation and interaction with its surroundings. The focus lies on the representation of the spatial knowledge gathered constantly over time in an indoor environment. However, from the background of research on an interactive service robot, methods for deployment in inference and verbal communication tasks are presented. The design and application of the models are guided by the requirements of referential communication. The approach here involves the analysis of the dynamic properties of structures in the robotâs field of view allowing it to distinguish objects of interest from other agents and background structures. The use of multiple persistent models representing these dynamic properties enables the robot to track changes in multiple scenes over time to establish spatial and temporal references. This work includes building a coherent representation considering allocentric and egocentric aspects of spatial knowledge for these models. Spatial analysis is extended with a semantic interpretation of objects and regions. This top-down approach for generating additional context information enhances the grounding process in communication. A holistic, boosting-based classification approach using a wide range of 2D and 3D visual features anchored in the spatial representation allows the system to identify room types. The process of grounding referential descriptions from a human interlocutor in the spatial representation is evaluated through referencing furniture. This method uses a probabilistic network for handling ambiguities in the descriptions and employs a strategy for resolving conflicts. In order to approve the real-world applicability of these approaches, this system was deployed on the mobile robot BIRON in a realistic apartment scenario involving observation and verbal interaction with an interlocutor
Exploring the Functional and Geometric Bias of Spatial Relations Using Neural Language Models
The challenge for computational models of spatial descriptions for situated dialogue systems is the integration of information from different modalities. The semantics of spatial descriptions are grounded in at least two sources of information: (i) a geometric representation of space and (ii) the functional interaction of related objects that. We train several neural language models on descriptions of scenes from a dataset of image captions and examine whether the functional or geometric bias of spatial descriptions reported in the literature is reflected in the estimated perplexity of these models. The results of these experiments have implications for the creation of models of spatial lexical semantics for human-robot dialogue systems. Furthermore, they also provide an insight into the kinds of the semantic knowledge captured by neural language models trained on spatial descriptions, which has implications for image captioning systems
Conceptual spatial representations for indoor mobile robots
We present an approach for creating conceptual representations of human-made indoor environments using mobile
robots. The concepts refer to spatial and functional properties of typical indoor environments. Following ďŹndings
in cognitive psychology, our model is composed of layers representing maps at diďŹerent levels of abstraction. The
complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition.
The system also incorporates a linguistic framework that actively supports the map acquisition process, and which
is used for situated dialogue. Finally, we discuss the capabilities of the integrated system
Interactive semantic mapping: Experimental evaluation
Robots that are launched in the consumer market need to provide more effective human robot interaction, and, in particular, spoken language interfaces. However, in order to support the execution of high level commands as they are specified in natural language, a semantic map is required. Such a map is a representation that enables the robot to ground the commands into the actual places and objects located in the environment. In this paper, we present the experimental evaluation of a system specifically designed to build semantically rich maps, through the interaction with the user. The results of the experiments not only provide the basis for a discussion of the features of the proposed approach, but also highlight the manifold issues that arise in the evaluation of semantic mapping
Knowledge Representation for Robots through Human-Robot Interaction
The representation of the knowledge needed by a robot to perform complex
tasks is restricted by the limitations of perception. One possible way of
overcoming this situation and designing "knowledgeable" robots is to rely on
the interaction with the user. We propose a multi-modal interaction framework
that allows to effectively acquire knowledge about the environment where the
robot operates. In particular, in this paper we present a rich representation
framework that can be automatically built from the metric map annotated with
the indications provided by the user. Such a representation, allows then the
robot to ground complex referential expressions for motion commands and to
devise topological navigation plans to achieve the target locations.Comment: Knowledge Representation and Reasoning in Robotics Workshop at ICLP
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Home alone: autonomous extension and correction of spatial representations
In this paper we present an account
of the problems faced by a mobile robot given
an incomplete tour of an unknown environment,
and introduce a collection of techniques which can
generate successful behaviour even in the presence
of such problems. Underlying our approach is the
principle that an autonomous system must be motivated
to act to gather new knowledge, and to validate
and correct existing knowledge. This principle is
embodied in Dora, a mobile robot which features
the aforementioned techniques: shared representations,
non-monotonic reasoning, and goal generation
and management. To demonstrate how well this
collection of techniques work in real-world situations
we present a comprehensive analysis of the Dora
systemâs performance over multiple tours in an indoor
environment. In this analysis Dora successfully
completed 18 of 21 attempted runs, with all but
3 of these successes requiring one or more of the
integrated techniques to recover from problems
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