Evaluating the Sim-to-Real Gap for Contact-Rich Robotic Manipulation Tasks using Suction Cups
Résumé
Despite the wide adoption of suction cups in automated logistic processes, their usage for contact-rich manipulation tasks is limited. This limitation is due to unpredictable large deformations of the suction cup that can arise in contact rich tasks, making motion planning an unsolved problem. A scenario in which these type of motions occur is in bin-packing. Allowing a robot to push or squeeze items into tight spaces would increase considerably the achievable packing density of current industrial bin-packing solutions, with important economic and environmental benefits. To enable robotic systems to perform these motions, we first integrate a newly developed compact 6D suction cup model into a physics engine. We subsequently evaluate the sim-to-real gap by evaluating object pose and force/torque predictions for contact-rich manipulation tasks against experiments where object motions are tracked with an accurate motion capture system and interaction forces are recorded by means of a force/torque sensor mounted on the tool arm. We show that, by the chosen physics engine and the suction cup model, these simulations can lead to high prediction accuracy, even for large deformations of the suction cup up to 30 degrees. By combining these simulations with sufficiently intelligent motion and packing algorithms, packing density performance far superior to the current state of the art and comparable to human operators can be achieved.
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