Learning to predict how rigid objects behave under simple manipulation
Learning to predict how rigid objects behave under simple manipulation
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学习预测刚性物体在简单操作下的行为
DOI：
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发表时间：
2011
期刊：
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通讯作者：
J. Wyatt
中科院分区：
文献类型：
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作者：
Marek Kopicki;Sebastian Zurek;R. Stolkin;Thomas Morwald;J. Wyatt
机器人操作中的一个重要问题是预测物体在操作动作下的行为的能力。这种能力是必要的，以允许规划的对象操作。物理模拟器可以用来做这件事，但它们对许多种类的物体相互作用的模拟很差。另一种方法是通过与对象交互来学习对象的运动模型。在本文中，我们解决的问题，学习预测的概率框架中的刚体的相互作用，并证明了结果在机器人推操纵域。机器人手臂对各种物体施加随机推力，并通过视觉系统观察所产生的运动。学习推动动作和物体运动之间的关系，并使机器人能够预测新的推动将导致的运动。这种学习并没有明确地使用物理知识，也没有任何预先编码的物理约束，甚至也不局限于遵守任何特定物理规则的领域。我们使用回归来有效地学习如何预测特定对象的总体运动。我们进一步展示了不同的密度函数可以编码不同种类的信息相互作用的对象的行为。通过将这些作为密度的产物相结合，我们展示了学习的预测器如何科普一定程度的泛化到以前未遇到的物体形状，受到以前未遇到的推动方向。性能通过在物理模拟器中的虚拟实验和配备有简单刚性手指的5轴臂的真实的实验的组合进行评估。
An important problem in robotic manipulation is the ability to predict how objects behave under manipulative actions. This ability is necessary to allow planning of object manipulations. Physics simulators can be used to do this, but they model many kinds of object interaction poorly. An alternative is to learn a motion model for objects by interacting with them. In this paper we address the problem of learning to predict the interactions of rigid bodies in a probabilistic framework, and demonstrate the results in the domain of robotic push manipulation. A robot arm applies random pushes to various objects and observes the resulting motion with a vision system. The relationship between push actions and object motions is learned, and enables the robot to predict the motions that will result from new pushes. The learning does not make explicit use of physics knowledge, or any pre-coded physical constraints, nor is it even restricted to domains which obey any particular rules of physics. We use regression to learn efficiently how to predict the gross motion of a particular object. We further show how different density functions can encode different kinds of information about the behaviour of interacting objects. By combining these as a product of densities, we show how learned predictors can cope with a degree of generalisation to previously unencountered object shapes, subjected to previously unencountered push directions. Performance is evaluated through a combination of virtual experiments in a physics simulator, and real experiments with a 5-axis arm equipped with a simple, rigid finger.