论文标题
机器人拾取位置，具有不确定的对象实例细分和形状完成
Robotic Pick-and-Place With Uncertain Object Instance Segmentation and Shape Completion
论文作者
论文摘要
我们考虑了部分可见的，新颖的物体的机器人拾取，其中目标放置是非平凡的，例如，将其紧密地包装到垃圾箱中。一种方法是（a）使用对象实例进行分割和形状完成来对象建模，（b）使用regrasp计划者来决定grasps，并将将模型置于其目标。但是，对于计划者而言，重要的是要解决感知模型中的不确定性，因为未观察到的区域中的对象几何形状只是猜测。我们通过将其纳入Regrasp Planner的成本函数来解释感知不确定性。我们比较七个不同的费用。其中之一，它使用神经网络来估计掌握和放置稳定性的可能性，它始终超过不确定性 - 统一成本，并且比蒙特卡洛采样更快地评估。在真正的机器人上，拟议的成本导致成功地将物体紧密地包装成垃圾箱7.8％，而通常使用的最低少量数量的成本则多7.8％。
We consider robotic pick-and-place of partially visible, novel objects, where goal placements are non-trivial, e.g., tightly packed into a bin. One approach is (a) use object instance segmentation and shape completion to model the objects and (b) use a regrasp planner to decide grasps and places displacing the models to their goals. However, it is critical for the planner to account for uncertainty in the perceived models, as object geometries in unobserved areas are just guesses. We account for perceptual uncertainty by incorporating it into the regrasp planner's cost function. We compare seven different costs. One of these, which uses neural networks to estimate probability of grasp and place stability, consistently outperforms uncertainty-unaware costs and evaluates faster than Monte Carlo sampling. On a real robot, the proposed cost results in successfully packing objects tightly into a bin 7.8% more often versus the commonly used minimum-number-of-grasps cost.
