Building of commonsense knowledge for home robotic services: 'bring something' and 'tidy things up'

Skulkittiyut, Weerachai; Lee, Haeyeon; Trung, Ngo Lam; Mizukawa, Makoto

doi:10.1504/ijma.2014.059770

Cited by 3 publications

(2 citation statements)

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“…In addition, heuristic approaches [9], [10] were proposed to sort and segregate deformable objects such as clothes piles according to the categories. In [11], a method has been proposed to represent basic human knowledge in order to improve the performance of tasks such as bringing an object or tidying a room by a robot. The idea is to structure the relationships between common activities and daily objects in a domestic environment.…”

Section: Related Workmentioning

confidence: 99%

A Quantifiable Stratification Strategy for Tidy-up in Service Robotics

Yan

Crombez

Buisson

et al. 2021

2021 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO)

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This paper addresses the problem of tidying up a living room in a messy condition with a service robot (i.e. domestic mobile manipulator). One of the key issues in completing such a task is how to continuously select the object to grasp and take it to the delivery area, especially when the robot works in constrained and partially observable environments. In this paper, we propose a quantifiable stratification method that allows the robot to find feasible action plans according to different configurations of objects-deposits, in order to smoothly deliver the objects to the target deposits. Specifically, it leverages a finite-state machine obeying the principle of Occam's razor (called O-FSM), which is designed to integrate arbitrary userdefined action plans typically ranging from simple to complex. Instead of considering a sophisticated model for the everchanging objects-deposits configuration in the tidy-up task, we empower the robot to make simple yet effective decisions based on its current faced configuration under a generalized framework. Through scenario planning and simulation experiments with the explicitly designed test cases based on the real robot and the real competition scene, the effectiveness of our method is illustrated.

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Section: Related Workmentioning

confidence: 99%

A Quantifiable Stratification Strategy for Tidy-up in Service Robotics

Yan

Crombez

Buisson

et al. 2021

2021 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO)

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“…McGuiness and Noy [27] provide five reasons to share common understanding of the structure of information among people or robots, to enable reuse of domain knowledge, to make domain assumptions explicit, to separate domain knowledge from the operational knowledge, and to analyses domain knowledge for the development of an ontology. Until now, many research efforts have been performed in order to apply Semantic Web technologies in various engineering fields for intelligent system development [18,19,30,31]. Once PDDL is integrated with Semantic Web, it could potentially benefit from such the ontologies.…”

Section: Semantic Reasoning For Role Constructionmentioning

confidence: 99%

Design and Implementation of Linked Planning Domain Definition Language

Tatsubori,

Munawar,

Moriyama

2019

Preprint

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Planning is a critical component of any artificial intelligence system that concerns the realization of strategies or action sequences typically for intelligent agents and autonomous robots. Given predefined parameterized actions, a planning service should accept a query with the goal and initial state to give a solution with a sequence of actions applied to environmental objects. This paper addresses the problem by providing a repository of actions generically applicable to various environmental objects based on Semantic Web technologies. Ontologies are used for asserting constraints in common sense as well as for resolving compatibilities between actions and states. Constraints are defined using Web standards such as SPARQL and SHACL to allow conditional predicates. We demonstrate the usefulness of the proposed planning domain description language with our robotics applications.

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Knowledge Engine for Robots Using Ontology

Salam

Munawar

2017

2017 5th Intl Conf on Applied Computing and Information Technology/4th Intl Conf on Computational Science/Intelligence and Appl

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Building of commonsense knowledge for home robotic services: 'bring something' and 'tidy things up'

Cited by 3 publications

References 0 publications

A Quantifiable Stratification Strategy for Tidy-up in Service Robotics

A Quantifiable Stratification Strategy for Tidy-up in Service Robotics

Design and Implementation of Linked Planning Domain Definition Language

Knowledge Engine for Robots Using Ontology

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