Visual Recognition of Objects and Control of Hand Shaping in Grasping Movements.

大脑必须解决两个重要的问题，在把握动作。第一个问题涉及到被抓取物体的识别。具体来说，大脑是如何整合视觉和运动信息的？第二个问题涉及手形规划。换句话说，大脑是如何根据物体的形状和任务来确定合适的手部姿势的？一个神经网络模型，解决了这样的问题已经开发出来。该网络由具有前向连接的多层神经元组成。神经网络的操作分为学习阶段和模式生成阶段。在学习阶段，通过整合视觉和体感信息，在网络的中间层形成所抓取物体的内部表征。在模式生成阶段，利用神经网络的松弛计算确定抓取物体的手指构型，并建立了一个用于获取抓取物体重量内部表征的神经网络模型.该模型由两个网络组成，一个由关节角度和力矩计算物体的重量，另一个由重量和关节角度计算力矩。结合这两个网络的多层网络可以用来学习手臂运动，而不需要知道抓取物体的重量。

The brain must solve two important problems in grasping movements. The first problem concerns the recognition of grasped objects. Specifically, how does the brain integrated visual and motor information on a grapsed object? The second problem concerns hand shape planning. In other words, how does the brain determine the suitable hand posture according to the shape of an object and the task?A neural network model which solves such problems has been developed. The network consists of multilayers of neurons with forward connections. The operations of the neural network are divided into the learning phase and the pattern generating phase. In the learning phase, internal representations of grasped objects are formed in the middle layer of the network by integrating visual and somatosensory information. In the pattern generating phase, the finger configuration for grasping an object is determined by using the relaxation computation of the network.A neural network model for acquiring an internal representation of the weight of grasped objects was also developed. This model consists of two networks; the one calculates weight of the object from joint angles and torques and the other calculates torques from the weight and joint angles. The multilayered network combining these two networks can be used to learn arm movements without knowing the weight of the grasped object.