This work investigates uncertainty-aware deep learning (DL) in tactile robotics based on a general framework introduced recently for robot vision. For a test scenario, we consider optical tactile sensing in combination with DL to estimate the edge pose as a feedback signal to servo around various 2D test objects. We demonstrate that uncertainty-aware DL can improve the pose estimation over deterministic DL methods. The system estimates the uncertainty associated with each prediction, which is used along with temporal coherency to improve the predictions via a Kalman filter, and hence improve the tactile servo control. The robot is able to robustly follow all of the presented contour shapes to reduce not only the error by a factor of two but also smooth the trajectory from the undesired noisy behaviour caused by previous deterministic networks. In our view, as the field of tactile robotics matures in its use of DL, the estimation of uncertainty will become a key component in the control of physically interactive tasks in complex environments.

中文翻译：

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用于机器人触摸的不确定性深度学习：在贝叶斯触觉伺服控制中的应用

这项工作基于最近为机器人视觉引入的通用框架，研究了触觉机器人技术中的不确定性感知深度学习（DL）。对于测试场景，我们考虑将光学触觉传感与DL结合使用，以估计边缘姿态，作为围绕各种2D测试对象的伺服反馈信号。我们证明了具有不确定性的DL可以比确定性DL方法改善姿态估计。系统估计与每个预测相关的不确定性，该不确定性与时间相干性一起用于通过卡尔曼滤波器改善预测，从而改善触觉伺服控制。该机器人能够稳健地跟踪所有呈现的轮廓形状，从而不仅可以将误差减少两倍，而且还可以消除由先前确定性网络引起的不希望有的噪声行为所产生的轨迹。