In this paper we consider joint perception and control of a pick-place system. It is important to consider perception and control jointly as some actions are more likely to succeed than others given non-uniform, perceptual uncertainty.Our approach is to combine 3D object instance segmentation and shape completion with classical regrasp planning. We use the perceptual modules to estimate their own uncertainty and then incorporate this uncertainty as a regrasp planning cost . 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.
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.
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In this paper we consider joint perception and control of a pick-place system. It is important to consider perception and control jointly as some actions are more likely to succeed than others given non-uniform, perceptual uncertainty.Our approach is to combine 3D
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
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with classical regrasp planning. We use the perceptual modules to estimate their own uncertainty and then incorporate this uncertainty as a regrasp planning cost
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
R
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
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