Abstract

Robots that interact with humans must learn to
not only adapt to different human partners but also to new
interactions. Such a form of learning can be achieved by
demonstrations and imitation. A recently introduced method
to learn interactions from demonstrations is the framework
of Interaction Primitives. While this framework is limited
to represent and generalize a single interaction pattern, in
practice, interactions between a human and a robot can consist
of many different patterns. To overcome this limitation this
paper proposes a Mixture of Interaction Primitives to learn
multiple interaction patterns from unlabeled demonstrations.
Specifically the proposed method uses Gaussian Mixture Models
of Interaction Primitives to model nonlinear correlations
between the movements of the different agents. We validate
our algorithm with two experiments involving interactive tasks
between a human and a lightweight robotic arm. In the first,
we compare our proposed method with conventional Interaction
Primitives in a toy problem scenario where the robot and the
human are not linearly correlated. In the second, we present a
proof-of-concept experiment where the robot assists a human
in assembling a box.