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.
翻译:我们考虑的是机器人选择和选择部分可见的、新颖的物体,其目标位置是非三重的,例如,紧紧地包装在垃圾桶中。一种方法是:(a) 使用对象实例分解和形状完成来模拟物体的模型;(b) 使用一个重写计划设计师来决定捕捉和使模型偏离其目标的位置。然而,对于规划员来说,关键是要考虑到所想象的模型的不确定性,因为在未观测到的区域内,对象的地理分布只是猜测而已。我们通过将它纳入重新grasp规划员的成本函数来计算概念上的不确定性。我们比较了七个不同的成本。其中之一是,使用神经网络来估计捕捉和定位稳定性的可能性,始终超过不确定性软件的成本,并比蒙特卡洛取样的速度快。在真正的机器人上,提出的成本结果是将物体成功地紧紧地装在一个垃圾箱中,比通常使用的最小数量成本高7.8%。