Robotic Pick-and-Place With Uncertain Object Instance Segmentation and Shape Completion
This addresses the problem of reliable robotic manipulation in cluttered environments for applications like logistics, though it is incremental as it builds on existing segmentation and planning methods.
The paper tackles robotic pick-and-place of partially visible novel objects by incorporating perceptual uncertainty into a regrasp planner's cost function, resulting in a 7.8% increase in successful tight packing compared to a baseline method.
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.