Incremental learning using partial feedback for gesture-based human-swarm interaction

Jawad Nagi, Hung Ngo, Alessandro Giusti, Luca M. Gambardella, Jurgen Schmidhuber, Gianni A. Di Caro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations


In this paper we consider a human-swarm interaction scenario based on hand gestures. We study how the swarm can incrementally learn hand gestures through the interaction with a human instructor providing training gestures and correction feedback. The main contribution of the paper is a novel incremental machine learning approach that makes the robot swarm learn and recognize the gestures in a distributed and decentralized fashion using binary (i.e., yes/no) feedback. It exploits cooperative information exchange and swarm's intrinsic parallelism and redundancy. We perform extensive tests using real gesture images, showing that good classification accuracies are obtained even with rather few training samples and relatively small swarms. We also show the good scalability of the approach and its relatively low requirements in terms of communication overhead. © 2012 IEEE.
Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Workshop on Robot and Human Interactive Communication
Number of pages8
StatePublished - Dec 14 2012
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14


Dive into the research topics of 'Incremental learning using partial feedback for gesture-based human-swarm interaction'. Together they form a unique fingerprint.

Cite this