{"ID":2878977,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.17466","arxiv_id":"2508.17466","title":"Optimizing Grasping in Legged Robots: A Deep Learning Approach to Loco-Manipulation","abstract":"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. By simulating thousands of interactions from various perspectives, we created pixel-wise annotated grasp-quality maps to serve as the ground truth for our model. This dataset was used to train a custom CNN with a U-Net-like architecture that processes multi-modal input from an onboard RGB and depth cameras, including RGB images, depth maps, segmentation masks, and surface normal maps. The trained model outputs a grasp-quality heatmap to identify the optimal grasp point. We validated the complete framework on a four-legged robot. The system successfully executed a full loco-manipulation task: autonomously navigating to a target object, perceiving it with its sensors, predicting the optimal grasp pose using our model, and performing a precise grasp. This work proves that leveraging simulated training with advanced sensing offers a scalable and effective solution for object handling.","short_abstract":"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 Ge...","url_abs":"https://arxiv.org/abs/2508.17466","url_pdf":"https://arxiv.org/pdf/2508.17466v3","authors":"[\"Dilermando Almeida\",\"Guilherme Lazzarini\",\"Juliano Negri\",\"Thiago H. Segreto\",\"Ricardo V. Godoy\",\"Marcelo Becker\"]","published":"2025-08-24T17:47:56Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"cs.CV\",\"cs.LG\",\"eess.SY\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}