scholarly journals Semantic Task Planning for Service Robots in Open Worlds

2021 ◽  
Vol 13 (2) ◽  
pp. 49
Author(s):  
Guowei Cui ◽  
Wei Shuai ◽  
Xiaoping Chen
Keyword(s):  
Language Processing ◽  
Domain Knowledge ◽  
Environmental Changes ◽  
General Purpose ◽  
Planning System ◽  
Service Robots ◽  
Service Robot ◽  
Dynamic Changes ◽  
World State ◽  
Open World

This paper presents a planning system based on semantic reasoning for a general-purpose service robot, which is aimed at behaving more intelligently in domains that contain incomplete information, under-specified goals, and dynamic changes. First, Two kinds of data are generated by Natural Language Processing module from the speech: (i) action frames and their relationships; (ii) the modifier used to indicate some property or characteristic of a variable in the action frame. Next, the task’s goals are generated from these action frames and modifiers. These goals are represented as AI symbols, combining world state and domain knowledge, which are used to generate plans by an Answer Set Programming solver. Finally, the plan’s actions are executed one by one, and continuous sensing grounds useful information, which makes the robot use contingent knowledge to adapt to dynamic changes and faults. For each action in the plan, the planner gets its preconditions and effects from domain knowledge, so during the execution of the task, the environmental changes, especially those conflict with the actions, not only the action being performed but also the subsequent actions, can be detected and handled as early as possible. A series of case studies are used to evaluate the system and verify its ability to acquire knowledge through dialogue with users, solve problems with the acquired causal knowledge, and plan for complex tasks autonomously in the open world.

scholarly journals Unified Software Platform for Intelligent Home Service Robots

2020 ◽  
Vol 10 (17) ◽  
pp. 5874
Author(s):  
Jae-Bong Yi ◽  
Taewoong Kang ◽  
Dongwoon Song ◽  
Seung-Joon Yi
Keyword(s):  
General Purpose ◽  
Service Robots ◽  
Service Robot ◽  
Physical Work ◽  
Software Framework ◽  
Mobile Manipulation ◽  
Autonomous Operation ◽  
Home Service ◽  
Manipulation Robots ◽  
Human Support

Although the mobile manipulation capability is crucial for a service robot to perform physical work without human support, the long-term autonomous operation of such a mobile manipulation robot in a real environment is still a tremendously difficult task. In this paper, we present a modular, general purpose software framework for intelligent mobile manipulation robots that can interact with humans using complex human speech commands; navigate smoothly in tight indoor spaces; and finally detect and manipulate various household objects and pieces of furniture autonomously. The suggested software framework is designed to be easily transferred to different home service robots, which include the Toyota Human Support Robot (HSR) and our Modular Service Robot-1 (MSR-1) platforms. It has successfully been used to solve various home service tasks at the RoboCup@Home and World Robot Summit international service robot competitions with promising results.

A REVIEW OF SENSOR TECHNOLOGY AND SENSOR FUSION METHODS FOR MAP-BASED LOCALIZATION OF SERVICE ROBOT

2016 ◽  
Vol 78 (7-5) ◽  
Author(s):  
Lim Thol Yong ◽  
Yeong Che Fai ◽  
Eileen Su Lee Ming
Keyword(s):  
Sensor Fusion ◽  
Optimal Path ◽  
Service Robots ◽  
Sensor Technology ◽  
Service Robot ◽  
Dynamic Changes ◽  
Localization Method ◽  
Comparative Review ◽  
Fusion Methods ◽  
Sensor Information

Service robot is currently gaining traction, particularly in hospitality, geriatric care and healthcare industries. The navigation of service robots requires high adaptability, flexibility and reliability. Hence, map-based navigation is suitable for service robot because of the ease in updating changes in environment and the flexibility in determining a new optimal path. For map-based navigation to be robust, an accurate and precise localization method is necessary. Localization problem can be defined as recognizing the robot’s own position in a given environment and is a crucial step in any navigational process. Major difficulties of localization include dynamic changes of the real world, uncertainties and limited sensor information. This paper presents a comparative review of sensor technology and sensor fusion methods suitable for map-based localization, focusing on service robot applications. 

The Art of Gate-Crashing

2009 ◽  
Vol 10 (3) ◽  
pp. 274-297 ◽  
Author(s):  
Helge Hüttenrauch ◽  
Elin A. Topp ◽  
Kerstin Severinson-Eklundh
Keyword(s):  
General Purpose ◽  
Human Robot Interaction ◽  
Service Robots ◽  
Service Robot ◽  
Laboratory Studies ◽  
Service Robotics ◽  
Robot Interaction ◽  
Sensory Data ◽  
Spatial Management ◽  
Personal Robot

Special purpose service robots have already entered the market and their users’ homes. Also the idea of the general purpose service robot or personal robot companion is increasingly discussed and investigated. To probe human–robot interaction with a mobile robot in arbitrary domestic settings, we conducted a study in eight different homes. Based on previous results from laboratory studies we identified particular interaction situations which should be studied thoroughly in real home settings. Based upon the collected sensory data from the robot we found that the different environments influenced the spatial management observable during our subjects’ interaction with the robot. We also validated empirically that the concept of spatial prompting can aid spatial management and communication, and assume this concept to be helpful for Human–Robot Interaction (HRI) design. In this article we report on our exploratory field study and our findings regarding, in particular, the spatial management observed during show episodes and movement through narrow passages. Keywords: COGNIRON, Domestic Service Robotics, Robot Field Trial, Human Augmented Mapping (HAM), Human–Robot Interaction (HRI), Spatial Management, Spatial Prompting

Combining Planning and Simulation to Create Human Robot Cooperative Processes with Industrial Service Robots

2016 ◽  
Vol 840 ◽  
pp. 91-98 ◽  
Author(s):  
Frank Heinze ◽  
Maike Klöckner ◽  
Nils Wantia ◽  
Jürgen Rossmann ◽  
Bernd Kuhlenkötter ◽  
...  
Keyword(s):  
Selection Process ◽  
Simulation System ◽  
Planning System ◽  
Service Robots ◽  
Service Robot ◽  
Collaborative Processes ◽  
Industrial Service ◽  
Feasible Solutions ◽  
Technical Solutions ◽  
System Selection

The integration of lightweight robots and service robots into industrial work places forms the new area of industrial service robots characterized by the transformation of manual processes to human robot interactive processes. Still, it is highly difficult to decide which manual processes are qualified for automation or collaboration. Consequently, only few collaborative processes can be found in industry. The objective of the project MANUSERV is to support decisions towards this transfer by developing a tool that supports users to identify practical technical solutions. The tool is a combination of a planning system and a simulation system. The aim of the planning system is to select technically feasible solutions and generate task sequences that solve the problem. The simulation system verifies the proposed solution and evaluates it technically, economically, and ergonomically. Manufacturers of service robots can provide their service robot specifications to be included into the system selection process.

scholarly journals Planning and Reinforcement Learning for General-Purpose Service Robots

2021 ◽  
Author(s):  
Yuqian Jiang
Keyword(s):  
Reinforcement Learning ◽  
General Purpose ◽  
Service Robots ◽  
Service Robot ◽  
Robot Systems ◽  
Significant Step ◽  
Robot Tasks ◽  
Learning To Reason ◽  
High Level ◽  
And Robotics

Despite recent progress in AI and robotics research, especially learned robot skills, there remain significant challenges in building robust, scalable, and general-purpose systems for service robots. This Ph.D. research aims to combine symbolic planning and reinforcement learning to reason about high-level robot tasks and adapt to the real world. We will introduce task planning algorithms that adapt to the environment and other agents, as well as reinforcement learning methods that are practical for service robot systems. Taken together, this work will make a significant step towards creating general-purpose service robots.

scholarly journals Improving the robustness of a service robot for continuous indoor monitoring: An incremental approach

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110121
Author(s):  
David Portugal ◽  
André G Araújo ◽  
Micael S Couceiro
Keyword(s):  
Real World ◽  
Service Robots ◽  
Service Robot ◽  
Technology Readiness ◽  
Incremental Approach ◽  
Technology Readiness Level ◽  
Readiness Level ◽  
Software And Hardware ◽  
Indoor Monitoring

To move out of the lab, service robots must reveal a proven robustness so they can be deployed in operational environments. This means that they should function steadily for long periods of time in real-world areas under uncertainty, without any human intervention, and exhibiting a mature technology readiness level. In this work, we describe an incremental methodology for the implementation of an innovative service robot, entirely developed from the outset, to monitor large indoor areas shared by humans and other obstacles. Focusing especially on the reliability of the fundamental localization system of the robot in the long term, we discuss all the incremental software and hardware features, design choices, and adjustments conducted, and show their impact on the performance of the robot in the real world, in three distinct 24-h long trials, with the ultimate goal of validating the proposed mobile robot solution for indoor monitoring.

A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura

2021 ◽  
pp. 016555152110077
Author(s):  
Sulong Zhou ◽  
Pengyu Kan ◽  
Qunying Huang ◽  
Janet Silbernagel
Keyword(s):  
Social Media ◽  
Real Time ◽  
Language Processing ◽  
Disaster Response ◽  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
Situational Awareness ◽  
High Performing ◽  
Latent Topics ◽  
Dirichlet Allocation

Natural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve real-time situational awareness (SA) information from tweets, the most effective way to mine text is using natural language processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high-performing supervised models require domain knowledge to specify categories and involve costly labelling tasks. This research proposes a guided latent Dirichlet allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model, LDA topics visualisation and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilised tweet information because they only assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multilabel references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.

scholarly journals Natural language processing methods for knowledge management—Applying document clustering for fast search and grouping of engineering documents

2021 ◽  
pp. 1063293X2098297
Author(s):  
Ivar Örn Arnarsson ◽  
Otto Frost ◽  
Emil Gustavsson ◽  
Mats Jirstrand ◽  
Johan Malmqvist
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Unstructured Data ◽  
Free Text ◽  
Engineering Change ◽  
Engineering Documents

Product development companies collect data in form of Engineering Change Requests for logged design issues, tests, and product iterations. These documents are rich in unstructured data (e.g. free text). Previous research affirms that product developers find that current IT systems lack capabilities to accurately retrieve relevant documents with unstructured data. In this research, we demonstrate a method using Natural Language Processing and document clustering algorithms to find structurally or contextually related documents from databases containing Engineering Change Request documents. The aim is to radically decrease the time needed to effectively search for related engineering documents, organize search results, and create labeled clusters from these documents by utilizing Natural Language Processing algorithms. A domain knowledge expert at the case company evaluated the results and confirmed that the algorithms we applied managed to find relevant document clusters given the queries tested.

The robot-to-robot service encounter: an examination of the impact of inter-robot warmth

2021 ◽  
Vol 35 (9) ◽  
pp. 15-27
Author(s):  
Magnus Söderlund
Keyword(s):  
Design Methodology ◽  
Service Robots ◽  
Service Robot ◽  
Service Encounters ◽  
Experimental Setting ◽  
Content Type ◽  
Robot Systems ◽  
Synthetic Agents ◽  
High Level ◽  
The Impact

Purpose This study aims to examine humans’ reactions to service robots’ display of warmth in robot-to-robot interactions – a setting in which humans’ impressions of a service robot will not only be based on what this robot does in relation to humans, but also on what it does to other robots. Design/methodology/approach Service robot display of warmth was manipulated in an experimental setting in such a way that a service robot A expressed low versus high levels of warmth in relation to another service robot B. Findings The results indicate that a high level of warmth expressed by robot A vis-à-vis robot B boosted humans’ overall evaluations of A, and that this influence was mediated by the perceived humanness and the perceived happiness of A. Originality/value Numerous studies have examined humans’ reactions when they interact with a service robot or other synthetic agents that provide service. Future service encounters, however, will comprise also multi-robot systems, which means that there will be many opportunities for humans to be exposed to robot-to-robot interactions. Yet, this setting has hitherto rarely been examined in the service literature.

Standard Method for Describing an Electronic Patient Record Template: Application of XML to Share Domain Knowledge

2000 ◽  
Vol 39 (01) ◽  
pp. 50-55 ◽  
Author(s):  
S. Yamazaki ◽  
Y. Satomura
Keyword(s):  
Standard Method ◽  
Clinical Guidelines ◽  
Domain Knowledge ◽  
Electronic Patient Record ◽  
Patient Record ◽  
Extensible Markup Language ◽  
Markup Language ◽  
Dynamic Changes ◽  
Extensible Markup

Abstract:A Template Definition Language (TDL) was developed to share knowledge of how to construct an electronic patient record (EPR) template. Based on the extensible markup language XML, TDL has been designed to be independent of EPR platforms or databases. Our research of TDL was conducted through evaluation of the description of various templates in the currently available EPRs and through comparisons with some electronic clinical guidelines. We conclude that TDL is sufficient for the objective but still needs improvement of the algorithm for describing dynamic changes.

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