This paper proposes a controller for stable grasping of unknown-shaped objects by two robotic fingers with tactile fingertips. The grasp is stabilised by rolling the fingertips on the contact surface and applying a desired grasping force to reach an equilibrium state. The validation is both in simulation and on a fully-actuated robot hand (the Shadow Modular Grasper) fitted with custom-built optical tactile sensors (based on the BRL TacTip). The controller requires the orientations of the contact surfaces, which are estimated by regressing a deep convolutional neural network over the tactile images. Overall, the grasp system is demonstrated to achieve stable equilibrium poses on various objects ranging in shape and softness, with the system being robust to perturbations and measurement errors. This approach also has promise to extend beyond grasping to stable in-hand object manipulation with multiple fingers.

翻译：本文建议用两个机器人手指用触摸式指尖稳定捕捉未知形物体的控制器。 抓取器通过在接触表面滚动指尖和运用一个想要的抓住力来达到平衡状态而稳定。 验证在模拟和完全活化的机器人手上( 影子模子格拉斯伯), 配有定制的光学触动感应器( 以 BRL TacTip 为基础 ) 。 控制器需要接触表面的方向, 而接触表面的方向是通过在触动图图像上重新侵蚀一个深层的进化神经网络来估计的。 总体而言, 抓取系统被证明能够在形状和柔软度不同的物体上实现稳定的平衡, 系统对扰动和测量错误非常强大。 这种方法还承诺超越掌握到多根手指稳定的手控物体操作。