论文标题
学习形状控制弹性可变形线性对象的控制形状
Learning Shape Control of Elastoplastic Deformable Linear Objects
论文作者
论文摘要
长期以来，可变形的物体操纵任务被视为具有挑战性的机器人问题。但是，直到最近，对这个主题的工作很少，大多数机器人操纵方法正在为刚性对象开发。可变形的对象更难建模和模拟，这限制了对模型的增强学习（RL）策略的使用，因为它们需要大量数据仅在模拟中得到满足。本文提出了针对可变形线性对象（DLOS）的新形状控制任务。更值得注意的是，我们介绍了弹性塑料对这种类型问题的影响的第一个研究。在各种应用中发现具有弹性性的物体（例如金属线），并且由于其非线性行为而具有挑战性。我们首先强调了从RL角度来解决此类操纵任务的挑战，尤其是在定义奖励方面。然后，基于微分几何形状的概念，我们提出了使用离散曲率和扭转的固有形状表示。最后，我们通过一项实证研究表明，为了成功地使用深层确定性策略梯度（DDPG）成功解决所提出的任务，奖励需要包括有关DLO形状的内在信息。
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.
