论文标题
用触觉感知插入的未知物体的手工操纵
In-Hand Manipulation of Unknown Objects with Tactile Sensing for Insertion
论文作者
论文摘要
在本文中，我们提出了一种使用触觉传感来操纵未知对象的方法，而无需依赖已知的对象模型。在许多情况下，仅视力的方法可能是不可行的。例如，由于凌乱的空间中的阻塞。我们通过引入一种使用触觉传感来重新定向未知对象的方法来解决此限制。它在任务驱动的操作过程中逐渐构建对物体形状和姿势的概率估计。我们的方法使用贝叶斯优化来平衡对全球对象形状的探索和有效的任务完成。为了证明我们的方法的有效性，我们将其应用于模拟触觉的滚子Grasper，这是一种在收集触觉数据时手动滚动对象的握把。我们通过随机生成的对象评估了插入任务的方法，并发现它可靠地重新设置对象，同时大大减少勘探时间。
In this paper, we present a method to manipulate unknown objects in-hand using tactile sensing without relying on a known object model. In many cases, vision-only approaches may not be feasible; for example, due to occlusion in cluttered spaces. We address this limitation by introducing a method to reorient unknown objects using tactile sensing. It incrementally builds a probabilistic estimate of the object shape and pose during task-driven manipulation. Our approach uses Bayesian optimization to balance exploration of the global object shape with efficient task completion. To demonstrate the effectiveness of our method, we apply it to a simulated Tactile-Enabled Roller Grasper, a gripper that rolls objects in hand while collecting tactile data. We evaluate our method on an insertion task with randomly generated objects and find that it reliably reorients objects while significantly reducing the exploration time.
