  Equipping robots with the ability to infer human intent is a vital
precondition for effective collaboration. Most computational approaches towards
this objective employ probabilistic reasoning to recover a distribution of
"intent" conditioned on the robot's perceived sensory state. However, these
approaches typically assume task-specific notions of human intent (e.g.
labelled goals) are known a priori. To overcome this constraint, we propose the
Disentangled Sequence Clustering Variational Autoencoder (DiSCVAE), a
clustering framework that can be used to learn such a distribution of intent in
an unsupervised manner. The DiSCVAE leverages recent advances in unsupervised
learning to derive a disentangled latent representation of sequential data,
separating time-varying local features from time-invariant global aspects.
Though unlike previous frameworks for disentanglement, the proposed variant
also infers a discrete variable to form a latent mixture model and enable
clustering of global sequence concepts, e.g. intentions from observed human
behaviour. To evaluate the DiSCVAE, we first validate its capacity to discover
classes from unlabelled sequences using video datasets of bouncing digits and
2D animations. We then report results from a real-world human-robot interaction
experiment conducted on a robotic wheelchair. Our findings glean insights into
how the inferred discrete variable coincides with human intent and thus serves
to improve assistance in collaborative settings, such as shared control.
