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.

翻译：本文提出一种利用触觉感知在手中操作未知物体的方法，无需依赖已知物体模型。在许多场景中，纯视觉方法可能不可行，例如因杂乱空间中的遮挡问题。我们通过引入一种利用触觉感知重新定向未知物体的方法来解决这一局限性。该方法在任务驱动操作过程中逐步构建物体形状与位姿的概率估计。采用贝叶斯优化平衡全局物体形状探索与高效任务完成。为验证方法有效性，我们将其应用于模拟的触觉滚筒夹具——一种可在手中滚动物体并收集触觉数据的夹持器。我们在随机生成物体的插入任务中评估该方法，结果表明该方法能可靠地重新定向物体，同时显著减少探索时间。