5,906 research outputs found
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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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 ļ¬ndings
in cognitive psychology, our model is composed of layers representing maps at diļ¬erent 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
Smart Exposition Rooms: The Ambient Intelligence View
We introduce our research on smart environments, in particular research on smart meeting rooms and investigate how research approaches here can be used in the context of smart museum environments. We distinguish the identification of domain knowledge, its use in sensory perception, its use in interpretation and modeling of events and acts in smart environments and we have some observations on off-line browsing and on-line remote participation in events in smart environments. It is argued that large-scale European research in the area of ambient intelligence will be an impetus to the research and development of smart galleries and museum spaces
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Continually improving grounded natural language understanding through human-robot dialog
As robots become ubiquitous in homes and workplaces such as hospitals and factories, they must be able to communicate with humans. Several kinds of knowledge are required to understand and respond to a human's natural language commands and questions. If a person requests an assistant robot to take me to Alice's office, the robot must know that Alice is a person who owns some unique office, and that take me means it should navigate there. Similarly, if a person requests bring me the heavy, green mug, the robot must have accurate mental models of the physical concepts heavy, green, and mug. To avoid forcing humans to use key phrases or words robots already know, this thesis focuses on helping robots understanding new language constructs through interactions with humans and with the world around them. To understand a command in natural language, a robot must first convert that command to an internal representation that it can reason with. Semantic parsing is a method for performing this conversion, and the target representation is often semantic forms represented as predicate logic with lambda calculus. Traditional semantic parsing relies on hand-crafted resources from a human expert: an ontology of concepts, a lexicon connecting language to those concepts, and training examples of language with abstract meanings. One thrust of this thesis is to perform semantic parsing with sparse initial data. We use the conversations between a robot and human users to induce pairs of natural language utterances with the target semantic forms a robot discovers through its questions, reducing the annotation effort of creating training examples for parsing. We use this data to build more dialog-capable robots in new domains with much less expert human effort (Thomason et al., 2015; Padmakumar et al., 2017). Meanings of many language concepts are bound to the physical world. Understanding object properties and categories, such as heavy, green, and mug requires interacting with and perceiving the physical world. Embodied robots can use manipulation capabilities, such as pushing, picking up, and dropping objects to gather sensory data about them. This data can be used to understand non-visual concepts like heavy and empty (e.g. get the empty carton of milk from the fridge), and assist with concepts that have both visual and non-visual expression (e.g. tall things look big and also exert force sooner than short things when pressed down on). A second thrust of this thesis focuses on strategies for learning these concepts using multi-modal sensory information. We use human-in-the-loop learning to get labels between concept words and actual objects in the environment (Thomason et al., 2016, 2017). We also explore ways to tease out polysemy and synonymy in concept words (Thomason and Mooney, 2017) such as light, which can refer to a weight or a color, the latter sense being synonymous with pale. Additionally, pushing, picking up, and dropping objects to gather sensory information is prohibitively time-consuming, so we investigate strategies for using linguistic information and human input to expedite exploration when learning a new concept (Thomason et al., 2018). Finally, we build an integrated agent with both parsing and perception capabilities that learns from conversations with users to improve both components over time. We demonstrate that parser learning from conversations (Thomason et al., 2015) can be combined with multi-modal perception (Thomason et al., 2016) using predicate-object labels gathered through opportunistic active learning (Thomason et al., 2017) during those conversations to improve performance for understanding natural language commands from humans. Human users also qualitatively rate this integrated learning agent as more usable after it has improved from conversation-based learning.Computer Science
BWIBots: A platform for bridging the gap between AI and humanārobot interaction research
Recent progress in both AI and robotics have enabled the development of general purpose robot platforms that are capable of executing a wide variety of complex, temporally extended service tasks in open environments. This article introduces a novel, custom-designed multi-robot platform for research on AI, robotics, and especially humanārobot interaction for service robots. Called BWIBots, the robots were designed as a part of the Building-Wide Intelligence (BWI) project at the University of Texas at Austin. The article begins with a description of, and justification for, the hardware and software design decisions underlying the BWIBots, with the aim of informing the design of such platforms in the future. It then proceeds to present an overview of various research contributions that have enabled the BWIBots to better (a) execute action sequences to complete user requests, (b) efficiently ask questions to resolve user requests, (c) understand human commands given in natural language, and (d) understand human intention from afar. The article concludes with a look forward towards future research opportunities and applications enabled by the BWIBot platform
Overcoming barriers and increasing independence: service robots for elderly and disabled people
This paper discusses the potential for service robots to overcome barriers and increase independence of
elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly
people and advances in technology which will make new uses possible and provides suggestions for some of these new
applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses
the complementarity of assistive service robots and personal assistance and considers the types of applications and
users for which service robots are and are not suitable
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