Abstract

Phase transitions in manipulation tasks often occur
when contacts between objects are made or broken. A
switch of the phase can result in the robot’s actions suddenly
influencing different aspects of its environment. Therefore, the
boundaries between phases often correspond to constraints or
subgoals of the manipulation task.
In this paper, we investigate how the phases of manipulation
tasks can be learned from data. The task is modeled as an
autoregressive hidden Markov model, wherein the hidden phase
transitions depend on the observed states. The model is learned
from data using the expectation-maximization algorithm. We
demonstrate the proposed method on both a pushing task
and a pepper mill turning task. The proposed approach was
compared to a standard autoregressive hidden Markov model.
The experiments show that the learned models can accurately
predict the transitions in phases during the manipulation tasks.