  Deformable object manipulation tasks have long been regarded as challenging
robotic problems. However, until recently very little work has been done on the
subject, with most robotic manipulation methods being developed for rigid
objects. Deformable objects are more difficult to model and simulate, which has
limited the use of model-free Reinforcement Learning (RL) strategies, due to
their need for large amounts of data that can only be satisfied in simulation.
This paper proposes a new shape control task for Deformable Linear Objects
(DLOs). More notably, we present the first study on the effects of
elastoplastic properties on this type of problem. Objects with elastoplasticity
such as metal wires, are found in various applications and are challenging to
manipulate due to their nonlinear behavior. We first highlight the challenges
of solving such a manipulation task from an RL perspective, particularly in
defining the reward. Then, based on concepts from differential geometry, we
propose an intrinsic shape representation using discrete curvature and torsion.
Finally, we show through an empirical study that in order to successfully solve
the proposed task using Deep Deterministic Policy Gradient (DDPG), the reward
needs to include intrinsic information about the shape of the DLO.

Robotics;Systems and Control;Systems and Control