Optimizing Grasping in Legged Robots: A Deep Learning Approach to Loco-Manipulation

ArXi:2508.17466v3 Announce Type: replace-cross This paper presents a deep learning framework designed to enhance the grasping capabilities of quadrupeds equipped with arms, with a focus on improving precision and adaptability. Our approach centers on a sim-to-real methodology that minimizes reliance on physical data collection. We developed a pipeline within the Genesis simulation environment to generate a synthetic dataset of grasp attempts on common objects.