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




1014Electrical EngineeringSouth Kensington Campus






BibTex format

author = {Petit, M and Fischer, T and Demiris, Y},
doi = {10.1109/TAMD.2015.2507439},
journal = {IEEE Transactions on Cognitive and Developmental Systems},
pages = {201--213},
title = {Lifelong Augmentation of Multi-Modal Streaming Autobiographical Memories},
url = {},
volume = {8},
year = {2015}

RIS format (EndNote, RefMan)

AB - Robot systems that interact with humans over extended periods of time will benefit from storing and recalling large amounts of accumulated sensorimotor and interaction data. We provide a principled framework for the cumulative organisation of streaming autobiographical data so that data can be continuously processed and augmented as the processing and reasoning abilities of the agent develop and further interactions with humans take place. As an example, we show how a kinematic structure learning algorithm reasons a-posteriori about the skeleton of a human hand. A partner can be asked to provide feedback about the augmented memories, which can in turn be supplied to the reasoning processes in order to adapt their parameters. We employ active, multi-modal remembering, so the robot as well as humans can gain insights of both the original and augmented memories. Our framework is capable of storing discrete and continuous data in real-time. The data can cover multiple modalities and several layers of abstraction (e.g. from raw sound signals over sentences to extracted meanings). We show a typical interaction with a human partner using an iCub humanoid robot. The framework is implemented in a platform-independent manner. In particular, we validate its multi platform capabilities using the iCub, Baxter and NAO robots. We also provide an interface to cloud based services, which allow automatic annotation of episodes. Our framework is geared towards the developmental robotics community, as it 1) provides a variety of interfaces for other modules, 2) unifies previous works on autobiographical memory, and 3) is licensed as open source software.
AU - Petit,M
AU - Fischer,T
AU - Demiris,Y
DO - 10.1109/TAMD.2015.2507439
EP - 213
PY - 2015///
SN - 2379-8920
SP - 201
TI - Lifelong Augmentation of Multi-Modal Streaming Autobiographical Memories
T2 - IEEE Transactions on Cognitive and Developmental Systems
UR -
UR -
VL - 8
ER -