Imperial College London

Professor Yiannis Demiris

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Human-Centred Robotics, Head of ISN



+44 (0)20 7594 6300y.demiris Website




1011Electrical EngineeringSouth Kensington Campus






BibTex format

author = {Zambelli, M and Demiris, Y},
doi = {10.1109/TCDS.2016.2624705},
journal = {IEEE Transactions on Cognitive and Developmental Systems},
pages = {113--126},
title = {Online multimodal ensemble learning using self-learned sensorimotor representations},
url = {},
volume = {9},
year = {2016}

RIS format (EndNote, RefMan)

AB - Internal models play a key role in cognitive agentsby providing on the one hand predictions of sensory consequencesof motor commands (forward models), and on the other handinverse mappings (inverse models) to realise tasks involvingcontrol loops, such as imitation tasks. The ability to predictand generate new actions in continuously evolving environmentsintrinsically requiring the use of different sensory modalities isparticularly relevant for autonomous robots, which must alsobe able to adapt their models online. We present a learningarchitecture based on self-learned multimodal sensorimotor rep-resentations. To attain accurate forward models, we propose anonline heterogeneous ensemble learning method that allows usto improve the prediction accuracy by leveraging differences ofmultiple diverse predictors. We further propose a method tolearn inverse models on-the-fly to equip a robot with multimodallearning skills to perform imitation tasks using multiple sensorymodalities. We have evaluated the proposed methods on aniCub humanoid robot. Since no assumptions are made on therobot kinematic/dynamic structure, the method can be appliedto different robotic platforms.
AU - Zambelli,M
AU - Demiris,Y
DO - 10.1109/TCDS.2016.2624705
EP - 126
PY - 2016///
SN - 2379-8920
SP - 113
TI - Online multimodal ensemble learning using self-learned sensorimotor representations
T2 - IEEE Transactions on Cognitive and Developmental Systems
UR -
UR -
VL - 9
ER -