Temporal Arm Tracking and Probabilistic Pointed Object Selection for Robot to Robot Interaction using Deictic Gestures

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • GIM Ltd.

Abstract

This paper discusses advancements on alternative communication methods between robots utilizing deictic gestures and body language. The provided approach boosts the performance of the formerly developed ROS-oriented system and grants increased accuracy and robustness. More precisely, the paper presents: a) a temporal tracking technique responsible to detect and track the arm of the pointing agent and b) a probabilistic pointed object selection method. On one hand, a model registration approach based on the Interactive Closest Point (ICP) algorithm, continuously tracks a detected pointing arm over time. On the other hand, a probabilistic method weights a number of features to estimate which of the available objects is the pointed one. The provided results indicate that the enhanced system outperforms the formerly developed approach and efficiently tackles any identified problems, such as the correct pointed object selection in ambiguous situations.

Details

Original languageEnglish
Title of host publication16th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2016
Publication statusPublished - 2 Jan 2017
MoE publication typeA4 Article in a conference publication
EventIEEE-RAS International Conference on Humanoid Robots - Cancun, Mexico
Duration: 15 Nov 201617 Nov 2016
Conference number: 16

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
PublisherInstitute of Electrical and Electronics Engineers, Inc.
ISSN (Print)2164-0572
ISSN (Electronic)2164-0580

Conference

ConferenceIEEE-RAS International Conference on Humanoid Robots
Abbreviated titleHumanoids 2016
CountryMexico
CityCancun
Period15/11/201617/11/2016

    Research areas

  • Modelling and simulating humans, Skill modelling, Social interaction and acceptability

ID: 9507848