This paper details the application of a forward model to improve a reaching task. The reaching task must be accomplished by a humanoid robot with 53 degrees of freedom (d.o.f.) and a stereo-vision system. We have explored via simulations a new way of constructing and utilizing a forward model that encodes eye–hand relationships. We constructed a forward model using the data obtained from only a single reaching attempt. ANFIS neural networks are used to construct the forward model, but the forward model is updated online with new information that comes from each reaching attempt. Using the obtained forward model, an initial image Jacobian is estimated and is used with a visual servoing controller. Simulation results demonstrate that errors are lower when the initial image Jacobian is derived from the forward model. This paper is one of the few attempts at applying visual servoing in a complete humanoid robot.

中文翻译：

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在视觉伺服中实现iCub人形机器人中到达任务的正向模型

本文详细介绍了正向模型在改善到达任务方面的应用。到达任务必须由具有53个自由度（dof）的人形机器人和立体视觉系统完成。通过仿真，我们探索了一种构建和利用对眼手关系进行编码的正向模型的新方法。我们使用仅一次到达尝试获得的数据构建了一个正向模型。ANFIS神经网络用于构建正向模型，但是正向模型会通过每次到达尝试获得的新信息在线更新。使用获得的正向模型，可以估计初始图像雅可比行列式，并将其与视觉伺服控制器一起使用。仿真结果表明，当从正向模型导出初始图像雅可比矩阵时，误差较低。