Diffusion-based learning theory for organizing visuo-motor coordination
Diffusion-based learning theory for organizing visuo-motor coordination
复制标题
用于组织视觉运动协调的基于扩散的学习理论
DOI：
10.1007/s004220050478
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发表时间：
1998
影响因子：
1.9
通讯作者：
Masami Ito
中科院分区：
文献类型：
--
作者：
Zhiwei Luo;Masami Ito
提出了一种基于扩散的学习理论，并将其用于组织具有冗余运动自由度(DOF)的眼手系统的视觉运动协调。该理论考虑了协调的空间最优性：最小化眼睛-手系统的末端执行器位置误差，以及在所有有限的工作空间上关节相对于末端执行器位置的角度的差异。通过引入关于空间的变分方法，我们得到了关节角度关于工作空间的偏微分方程式。该方程包括一个扩散项。对于给定的边界条件和初始条件，其解是唯一的，且解是一个组织良好的映射。从运动学习的角度来看，我们的方法包括监督学习和自组织两个方面。首先，我们假设从手系统的关节角度到末端执行器位置的正向关系可以通过有监督的学习获得，并且在工作空间的边界处，监督者可以提供正确的关节信息。然后，通过演化扩散方程来组织视觉运动的协调。我们使用3-DOF比例操纵器展示了这种方法的有效性。文中还讨论了如何实现可视化运动映射、如何在多种运动中利用合成映射、初始条件对映射形成的影响以及与边界条件的关系等问题。我们的方法有三个优点：(1)它不需要对眼睛-手系统进行太多的尝试运动；(2)在地图形成过程中，它只需要每个节点之间的局部交互；(3)它保证了最终地图在所有有界工作空间上的空间最优性。
A diffusion-based learning theory is presented and applied to organize the visuomotor coordination of an eye-hand system which has redundant motion degree of freedom (dof). This theory considers the spatial optimality of the coordination: to minimize the end-effector position error of the eye-hand system as well as the differentiation of the joint angles with respect to the end-effector positions over all the bounded work space. By introducing variational methods with respect to the space, we derive a partial differential equation (PDE) of the joint angles with respect to the work space. The equation includes a diffusion term. For the given boundary conditions and the initial conditions, it can be solved uniquely, and the solution is a well organized map. From the motor learning point of view, our approach contains both the aspects of supervised learning as well as self-organization. Firstly, we assume that the forward relation from the hand system's joint angles to its end-effector positions can be obtained using supervised learning, and at the boundary of the work space, the supervisor can provide correct joint information. Then, by evolving the diffusion equation, we organize the visuomotor coordination. We show the effectiveness of this approach using a 3-dof scale manipulator. The problems of how to realize the visuomotor map; how to utilize the resultant map in several motions; and what are the influences of the initial conditions on the map formation and the relation to the boundary conditions are also discussed using computer simulations. Our approach has three advantages: (1) it does not require too many trial motions for the eye-hand system; (2) during the map formation process, it requires only the local interactions between each node; and (3) it guarantees the final map's spatial optimality over all the bounded work space.