OptiGrasp: Optimized Grasp Pose Detection Using RGB Images for Warehouse Picking Robots
This addresses the need for cost-effective and adaptable robotic grasping in warehouse automation, though it appears incremental as it builds on existing foundation models.
The paper tackles the problem of robust object picking for warehouse robots by developing a method that uses only RGB images instead of depth sensors, achieving an 82.3% success rate in real-world applications with generalization to novel objects.
In warehouse environments, robots require robust picking capabilities to manage a wide variety of objects. Effective deployment demands minimal hardware, strong generalization to new products, and resilience in diverse settings. Current methods often rely on depth sensors for structural information, which suffer from high costs, complex setups, and technical limitations. Inspired by recent advancements in computer vision, we propose an innovative approach that leverages foundation models to enhance suction grasping using only RGB images. Trained solely on a synthetic dataset, our method generalizes its grasp prediction capabilities to real-world robots and a diverse range of novel objects not included in the training set. Our network achieves an 82.3\% success rate in real-world applications. The project website with code and data will be available at http://optigrasp.github.io.