Next Article in Journal
A Spatial Queuing-Based Algorithm for Multi-Robot Task Allocation
Previous Article in Journal / Special Issue
Leveraging Qualitative Reasoning to Learning Manipulation Tasks
Article Menu

Export Article

Open AccessArticle
Robotics 2015, 4(3), 284-315; doi:10.3390/robotics4030284

Intent Understanding Using an Activation Spreading Architecture

Computer Science and Engineering Department, University of Nevada Reno, Reno, NV 89557, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Nicola Bellotto, Nick Hawes, Mohan Sridharan and Daniele Nardi
Received: 1 March 2015 / Revised: 11 July 2015 / Accepted: 15 July 2015 / Published: 30 July 2015
(This article belongs to the Special Issue Representations and Reasoning for Robotics)

Abstract

In this paper, we propose a new approach for recognizing intentions of humans by observing their activities with a color plus depth (RGB-D) camera. Activities and goals are modeled as a distributed network of inter-connected nodes in an Activation Spreading Network (ASN). Inspired by a formalism in hierarchical task networks, the structure of the network captures the hierarchical relationship between high-level goals and low-level activities that realize these goals. Our approach can detect intentions before they are realized and it can work in real-time. We also extend the formalism of ASNs to incorporate contextual information into intent recognition. We further augment the ASN formalism with special nodes and synaptic connections to model ordering constraints between actions, in order to represent and handle partial-order plans in our ASN. A fully functioning system is developed for experimental evaluation. We implemented a robotic system that uses our intent recognition to naturally interact with the user. Our ASN based intent recognizer is tested against three different scenarios involving everyday activities performed by a subject, and our results show that the proposed approach is able to detect low-level activities and recognize high-level intentions effectively in real-time. Further analysis shows that contextual and partial-order ASNs are able to discriminate between otherwise ambiguous goals. View Full-Text
Keywords: intent recognition; activation spreading network; activity recognition; scene understanding intent recognition; activation spreading network; activity recognition; scene understanding
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Saffar, M.T.; Nicolescu, M.; Nicolescu, M.; Rekabdar, B. Intent Understanding Using an Activation Spreading Architecture. Robotics 2015, 4, 284-315.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Robotics EISSN 2218-6581 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top