Before revision:  We compare 7 different regrasp planning cost functions, 4 of which explicitly model probability of plan execution success. Results show uncertainty-aware costs improve performance for complex tasks, e. g., for a bin packing task, object placement success is 6.2\% higher in simulation and 4.1\% higher in the real world with an uncertainty-aware cost versus the commonly used minimum-number-of-grasps cost.
After revision:  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.