Encoding cloth manipulations using a graph of states and transitions
This work addresses the challenge of cloth manipulation in robotics, which is incremental as it builds on existing methods for representation and learning.
The authors tackled the problem of representing cloth manipulation for domestic robots by proposing a generic, compact graph-based representation of states and transitions, which they used to encode two tasks learned from human data and derived meaningful motion primitives.
Cloth manipulation is very relevant for domestic robotic tasks, but it presents many challenges due to the complexity of representing, recognizing and predicting the behaviour of cloth under manipulation. In this work, we propose a generic, compact and simplified representation of the states of cloth manipulation that allows for representing tasks as sequences of states and transitions. We also define a Cloth Manipulation Graph that encodes all the strategies to accomplish a task. Our novel representation is used to encode two different cloth manipulation tasks, learned from an experiment with human subjects with video and motion data. We show how our simplified representation allows to obtain a map of meaningful motion primitives.