Abstract

This paper proposes a probabilistic framework
based on movement primitives for robots that work in collaboration
with a human coworker. Since the human coworker
can execute a variety of unforeseen tasks a requirement of our
system is that the robot assistant must be able to adapt and
learn new skills on-demand, without the need of an expert
programmer. Thus, this paper leverages on the framework
of imitation learning and its application to human-robot interaction
using the concept of Interaction Primitives (IPs).
We introduce the use of Probabilistic Movement Primitives
(ProMPs) to devise an interaction method that both recognizes
the action of a human and generates the appropriate movement
primitive of the robot assistant. We evaluate our method
on experiments using a lightweight arm interacting with a
human partner and also using motion capture trajectories of
two humans assembling a box. The advantages of ProMPs in
relation to the original formulation for interaction are exposed
and compared.