@inproceedings{Zolotas:2022,
author = {Zolotas, M and Demiris, Y},
pages = {9814--9820},
publisher = {IEEE},
title = {Disentangled sequence clustering for human intention inference},
url = {https://ieeexplore.ieee.org/abstract/document/9981116},
year = {2022}
}

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TY  - CPAPER
AB  - Equipping robots with the ability to infer human intent is a vital precondition for effective collaboration. Most computational approaches towards this objective derive a probability distribution of “intent” conditioned on the robot’s perceived state. However, these approaches typically assumetask-specific labels of human intent are known a priori. To overcome this constraint, we propose the Disentangled Sequence Clustering Variational Autoencoder (DiSCVAE), a clustering framework capable of learning such a distribution of intent in an unsupervised manner. The proposed framework leverages recent advances in unsupervised learning to disentangle latentrepresentations of sequence data, separating time-varying local features from time-invariant global attributes. As a novel extension, the DiSCVAE also infers a discrete variable to form a latent mixture model and thus enable clustering over these global sequence concepts, e.g. high-level intentions. We evaluate the DiSCVAE on a real-world human-robot interaction datasetcollected using a robotic wheelchair. Our findings reveal that the inferred discrete variable coincides with human intent, holding promise for collaborative settings, such as shared control.
AU  - Zolotas,M
AU  - Demiris,Y
EP  - 9820
PB  - IEEE
PY  - 2022///
SN  - 2153-0866
SP  - 9814
TI  - Disentangled sequence clustering for human intention inference
UR  - https://ieeexplore.ieee.org/abstract/document/9981116
UR  - http://hdl.handle.net/10044/1/100490
ER  - 

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