DYNAMO-GRASP: DYNAMics-aware Optimization for GRASP Point Detection in Suction Grippers

Boling Yang, Soofiyan Atar, Markus Grotz, Byron Boots, Joshua Smith

Keywords: Suction Grasping, Manipulation, Deep Learning, Vision

Abstract: In this research, we introduce a novel approach to the challenge of suction grasp point detection. Our method, exploiting the strengths of physics-based simulation and data-driven modeling, accounts for object dynamics during the grasping process, markedly enhancing the robot's capability to handle previously unseen objects and scenarios in real-world settings. We benchmark DYNAMO-GRASP against established approaches via comprehensive evaluations in both simulated and real-world environments. DYNAMO-GRASP delivers improved grasping performance with greater consistency in both simulated and real-world settings. Remarkably, in real-world tests with challenging scenarios, our method demonstrates a success rate improvement of up to 48\% over SOTA methods. Demonstrating a strong ability to adapt to complex and unexpected object dynamics, our method offers robust generalization to real-world challenges. The results of this research set the stage for more reliable and resilient robotic manipulation in intricate real-world situations. Experiment videos, dataset, model, and code are available at: https://sites.google.com/view/dynamo-grasp.

Student First Author: yes

Instructions: I have read the instructions for authors (https://corl2023.org/instructions-for-authors/)

Website: https://sites.google.com/view/dynamo-grasp

Code: https://github.com/dynamo-grasp/dynamo-grasp