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

Published: 30 Aug 2023, Last Modified: 24 Oct 2023CoRL 2023 PosterReaders: Everyone
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
Publication Agreement: pdf
Poster Spotlight Video: mp4
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