Context-specific intention awareness through web query in robotic caregiving

Liu, Rui; Zhang, Xiaoli; Webb, Jeremy D.; Li, Songpo

doi:10.1109/icra.2015.7139455

Cited by 15 publications

(18 citation statements)

References 25 publications

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“…Moreover, to make the FCII method more capable of solving practical problems in general real-world situations, efforts have been put in our paper on improving the method's general applicability, summarized as follows (''Discussion'' section). (a) The fuzzy method is developed for generating context feature statuses from different types of sensory data, as shown in equations (1) to (4). With statistical learning methods in equations (10) and (11), the sensory data was interpreted into intention-related knowledge by measuring the involvements and influences of these statuses given a human intention.…”

Section: Discussionmentioning

confidence: 99%

“…The fuzzy function m pk for C p consists of several fuzzy status memberships M pk ðC p Þ, shown in equations (3) and (4) and Figure 3. The values of 1 , 2 ; 3 , and 4 are defined by volunteers' subjective judgments, shown in equations (1) to (4). For a single feature status ranging 1 -4 identified by all the volunteers, the top @% of the data ranging 3 -4 and the bottom @% of the data ranging 1 -2 are with linearly changed fuzzy membership values.…”

Section: Context Processingmentioning

confidence: 99%

“…2 Given this situation, assistance robots that help the elderly with activities of daily living (ADLs) have been introduced to improve the elderly's quality of life. 4,5,6 Typical applications include navigation robots, 7 rehabilitation robots, 1,8 and smart reminders. 9,10 Robotic caregiving requires robots to be sufficiently intelligent in order to implicitly and naturally understand a user's intentions with minimal human physical involvements.…”

Section: Robotic Elderly Caregivingmentioning

confidence: 99%

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Fuzzy context-specific intention inference for robotic caregiving

Liu

Zhang

2016

International Journal of Advanced Robotic Systems

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To provide timely and appropriate assistance, robots must have the capability of proactively understanding a user's personal needs, the so-called human intention inference. In human-human interaction, humans have a natural and implicit way to infer others' intentions by selecting correlated context features and interpreting these features based on their life experience. However, robots do not have this capability and it is not realistic to build an explicit formula to associate human intentions with context. In this article, a novel fuzzy context-specific intention inference method is developed for human-like implicit human intention inference. With a fuzzy manner, context features are converted into discrete context statuses, which are similar to human subjective feelings. An intention-centered common sense database is developed consisting of correlated fuzzy context statuses, object affordances, and their relationship with human intentions. With this database, a Fuzzy Naïve Bayesian Network algorithm is adopted for implicit intention inference. Home scenario results validated the fuzzy context-specific intention inference methods reliability and lab scenario results validated the fuzzy context-specific intention inference methods effectiveness and robustness. This work is expected to develop intuitive and effective human-robot interaction, consequently enhancing the adoption of assistive technologies and improving the independence of the disabled and elderly in activities of daily living.

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Section: Discussionmentioning

confidence: 99%

Section: Context Processingmentioning

confidence: 99%

Section: Robotic Elderly Caregivingmentioning

confidence: 99%

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Fuzzy context-specific intention inference for robotic caregiving

Liu

Zhang

2016

International Journal of Advanced Robotic Systems

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“…The locational information (pose of individual objects) is subsymbolic knowledge obtained from sensors, such as RGB-D camera, and 3D spatial relations are abstracted symbolic knowledge that must be derived from this locational information. 3D spatial relations are comprehensive knowledge commonly required in most robot domains and are essential prior knowledge for deriving complex context knowledge, which is more difficult to determine, for example, the intention of external agents [10,11]. However, 3D spatial relations must be tracked in real time because they continuously change with time independent of the robot, and must be able to store and retrieve past context knowledge.…”

Section: Introductionmentioning

confidence: 99%

A Robotic Context Query-processing Framework based on Spatio-temporal Context Ontology

Lee

Kim

2018

Preprint

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Service robots operating in indoor environments should recognize dynamic changes from sensors, such as RGB-D camera, and recall the past context. Therefore, we propose a context query-processing framework, comprising spatio-temporal robotic context query language (ST-RCQL) and spatio-temporal robotic context query-processing system (ST-RCQP), for service robots. We designed them based on the spatio-temporal context ontology. ST-RCQL can query not only the current context knowledge but also the past. In addition, ST-RCQL includes a variety of time operators and time constants, and thus queries can be written very efficiently. The ST-RCQP is a query-processing system equipped with a perception handler, working memory, and backward reasoner for real-time query-processing. Moreover, ST-RCQP accelerates query-processing speed by building a spatio-temporal index in the working memory, where percepts are stored. Through various qualitative and quantitative experiments, we demonstrate the high efficiency and performance of the proposed context query-processing framework.

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“…The locational information (pose of individual objects) is sub-symbolic knowledge obtained from sensors, such as RGB-depth (RGB-D) cameras, and 3D spatial relations are abstracted symbolic knowledge that must be derived from this locational information. Three-dimensional spatial relations are comprehensive knowledge commonly required in most robot domains and are essential prior knowledge for deriving complex context knowledge, which is more difficult to determine; for example, the intention of external agents [ 10 , 11 ]. However, 3D spatial relations must be tracked in real time because they continuously change time independently of the robot and must be able to store and retrieve past context knowledge.…”

Section: Introductionmentioning

confidence: 99%

A Robotic Context Query-Processing Framework Based on Spatio-Temporal Context Ontology

Lee

Kim

2018

Sensors

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Service robots operating in indoor environments should recognize dynamic changes from sensors, such as RGB-depth (RGB-D) cameras, and recall the past context. Therefore, we propose a context query-processing framework, comprising spatio-temporal robotic context query language (ST-RCQL) and a spatio-temporal robotic context query-processing system (ST-RCQP), for service robots. We designed them based on spatio-temporal context ontology. ST-RCQL can query not only the current context knowledge, but also the past. In addition, ST-RCQL includes a variety of time operators and time constants; thus, queries can be written very efficiently. The ST-RCQP is a query-processing system equipped with a perception handler, working memory, and backward reasoner for real-time query-processing. Moreover, ST-RCQP accelerates query-processing speed by building a spatio-temporal index in the working memory, where percepts are stored. Through various qualitative and quantitative experiments, we demonstrate the high efficiency and performance of the proposed context query-processing framework.

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Context-specific intention awareness through web query in robotic caregiving

Cited by 15 publications

References 25 publications

Fuzzy context-specific intention inference for robotic caregiving

Fuzzy context-specific intention inference for robotic caregiving

A Robotic Context Query-processing Framework based on Spatio-temporal Context Ontology

A Robotic Context Query-Processing Framework Based on Spatio-Temporal Context Ontology

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