Cargando…

Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework

From social dining in households to product assembly in manufacturing lines, goal-directed reasoning and cooperation with other agents in shared workspaces is a ubiquitous aspect of our day-to-day activities. Critical for such behaviours is the ability to spontaneously anticipate what is doable by o...

Descripción completa

Detalles Bibliográficos
Autores principales: Bhat, Ajaz A., Mohan, Vishwanathan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6096944/
https://www.ncbi.nlm.nih.gov/pubmed/30147802
http://dx.doi.org/10.1007/s12559-018-9553-1
_version_ 1783348202790977536
author Bhat, Ajaz A.
Mohan, Vishwanathan
author_facet Bhat, Ajaz A.
Mohan, Vishwanathan
author_sort Bhat, Ajaz A.
collection PubMed
description From social dining in households to product assembly in manufacturing lines, goal-directed reasoning and cooperation with other agents in shared workspaces is a ubiquitous aspect of our day-to-day activities. Critical for such behaviours is the ability to spontaneously anticipate what is doable by oneself as well as the interacting partner based on the evolving environmental context and thereby exploit such information to engage in goal-oriented action sequences. In the setting of an industrial task where two robots are jointly assembling objects in a shared workspace, we describe a bioinspired neural architecture for goal-directed action planning based on coupled interactions between multiple internal models, primarily of the robot’s body and its peripersonal space. The internal models (of each robot’s body and peripersonal space) are learnt jointly through a process of sensorimotor exploration and then employed in a range of anticipations related to the feasibility and consequence of potential actions of two industrial robots in the context of a joint goal. The ensuing behaviours are demonstrated in a real-world industrial scenario where two robots are assembling industrial fuse-boxes from multiple constituent objects (fuses, fuse-stands) scattered randomly in their workspace. In a spatially unstructured and temporally evolving assembly scenario, the robots employ reward-based dynamics to plan and anticipate which objects to act on at what time instances so as to successfully complete as many assemblies as possible. The existing spatial setting fundamentally necessitates planning collision-free trajectories and avoiding potential collisions between the robots. Furthermore, an interesting scenario where the assembly goal is not realizable by either of the robots individually but only realizable if they meaningfully cooperate is used to demonstrate the interplay between perception, simulation of multiple internal models and the resulting complementary goal-directed actions of both robots. Finally, the proposed neural framework is benchmarked against a typically engineered solution to evaluate its performance in the assembly task. The framework provides a computational outlook to the emerging results from neurosciences related to the learning and use of body schema and peripersonal space for embodied simulation of action and prediction. While experiments reported here engage the architecture in a complex planning task specifically, the internal model based framework is domain-agnostic facilitating portability to several other tasks and platforms. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12559-018-9553-1) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-6096944
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Springer US
record_format MEDLINE/PubMed
spelling pubmed-60969442018-08-24 Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework Bhat, Ajaz A. Mohan, Vishwanathan Cognit Comput Article From social dining in households to product assembly in manufacturing lines, goal-directed reasoning and cooperation with other agents in shared workspaces is a ubiquitous aspect of our day-to-day activities. Critical for such behaviours is the ability to spontaneously anticipate what is doable by oneself as well as the interacting partner based on the evolving environmental context and thereby exploit such information to engage in goal-oriented action sequences. In the setting of an industrial task where two robots are jointly assembling objects in a shared workspace, we describe a bioinspired neural architecture for goal-directed action planning based on coupled interactions between multiple internal models, primarily of the robot’s body and its peripersonal space. The internal models (of each robot’s body and peripersonal space) are learnt jointly through a process of sensorimotor exploration and then employed in a range of anticipations related to the feasibility and consequence of potential actions of two industrial robots in the context of a joint goal. The ensuing behaviours are demonstrated in a real-world industrial scenario where two robots are assembling industrial fuse-boxes from multiple constituent objects (fuses, fuse-stands) scattered randomly in their workspace. In a spatially unstructured and temporally evolving assembly scenario, the robots employ reward-based dynamics to plan and anticipate which objects to act on at what time instances so as to successfully complete as many assemblies as possible. The existing spatial setting fundamentally necessitates planning collision-free trajectories and avoiding potential collisions between the robots. Furthermore, an interesting scenario where the assembly goal is not realizable by either of the robots individually but only realizable if they meaningfully cooperate is used to demonstrate the interplay between perception, simulation of multiple internal models and the resulting complementary goal-directed actions of both robots. Finally, the proposed neural framework is benchmarked against a typically engineered solution to evaluate its performance in the assembly task. The framework provides a computational outlook to the emerging results from neurosciences related to the learning and use of body schema and peripersonal space for embodied simulation of action and prediction. While experiments reported here engage the architecture in a complex planning task specifically, the internal model based framework is domain-agnostic facilitating portability to several other tasks and platforms. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12559-018-9553-1) contains supplementary material, which is available to authorized users. Springer US 2018-04-14 2018 /pmc/articles/PMC6096944/ /pubmed/30147802 http://dx.doi.org/10.1007/s12559-018-9553-1 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Bhat, Ajaz A.
Mohan, Vishwanathan
Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework
title Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework
title_full Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework
title_fullStr Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework
title_full_unstemmed Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework
title_short Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework
title_sort goal-directed reasoning and cooperation in robots in shared workspaces: an internal simulation based neural framework
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6096944/
https://www.ncbi.nlm.nih.gov/pubmed/30147802
http://dx.doi.org/10.1007/s12559-018-9553-1
work_keys_str_mv AT bhatajaza goaldirectedreasoningandcooperationinrobotsinsharedworkspacesaninternalsimulationbasedneuralframework
AT mohanvishwanathan goaldirectedreasoningandcooperationinrobotsinsharedworkspacesaninternalsimulationbasedneuralframework