Learning a tool's homogeneous transformation by tactile-based interaction
Learning a tool's homogeneous transformation by tactile-based interaction
复制标题
通过基于触觉的交互来学习工具的同质变换
DOI：
10.1109/humanoids.2016.7803309
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
H. Ritter
中科院分区：
文献类型：
--
作者：
Qiang Li;R. Haschke;H. Ritter
我们提出了一种基于触觉的操作策略，利用覆盖在工具表面的触觉矩阵传感器提供的触觉感知来学习抓取刚性工具的齐次变换。利用自学习触觉伺服控制器，机器人可以安全地使用触觉工具来实现不同的基于触觉的探索原语（EPs）。将EPs作为输入，观察触觉接触作为输出，机器人可以鲁棒估计刀具的齐次变换。将学习到的变换与已知的机器人运动学模型相结合，形成新的刀具操作运动学链，从而向机器人柔性刀具使用的“塑性体图式”迈进了一步。在假设测量仅受高斯白噪声污染的情况下，对该方法的可行性和鲁棒性进行了数值评估，然后用KUKA LWR和SCHUNK SDH-2手抓取刚性触觉工具的实际机器人装置对该方法进行了评估。利用新的操作链，我们演示了两种触觉工具的伺服实验：反应滑动和滚动工具和跟踪未知物体边缘。
We propose a tactile-based manipulation strategy to learn the homogeneous transformation of a grasped rigid tool, using tactile sensing delivered through a tactile matrix sensor covering the tool surface. Exploiting the self-learning tactile servoing controller, a robot safely use the tactile tool to implement different tactile-based exploration primitives (EPs). Considering EPs as input and observing the tactile contacts as output, the robot can robustly estimate the tool's homogeneous transformation. The learned transformation are combined with the known robot's kinematics model to form a new tool manipulation kinematics chain, thereby realizing a step towards a “plastic body schema” for flexible tool use by a robot. We numerically evaluate the method's feasibility and robustness assuming that measurements are only polluted by Gaussian white noise, then evaluate the proposed method with a real robot setup - a KUKA LWR and a SCHUNK SDH-2 hand grasping a rigid tactile tool. With the new manipulation chain, we demonstrate two tactile tool's servoing experiments: reactively sliding and rolling tool and tracking an unknown object edge.
影响因子：
4.3
作者：
Buescher, Gereon H.;Koiva, Risto;Ritter, Helge J.
通讯作者：
Ritter, Helge J.