Robotic Pick-and-Place With Uncertain Object Instance Segmentation and Shape Completion | IEEE Journals & Magazine | IEEE Xplore

Robotic Pick-and-Place With Uncertain Object Instance Segmentation and Shape Completion


Abstract:

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 ob...Show More

Abstract:

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.
Published in: IEEE Robotics and Automation Letters ( Volume: 6, Issue: 2, April 2021)
Page(s): 1753 - 1760
Date of Publication: 19 February 2021

ISSN Information:

PubMed ID: 33834114

Funding Agency:


Contact IEEE to Subscribe

References

References is not available for this document.