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

• URL: http://arxiv.org/abs/2508.17466v2
• Date: Sat, 11 Oct 2025 16:20:50 GMT
• Title: Optimizing Grasping in Legged Robots: A Deep Learning Approach to Loco-Manipulation
• Authors: Dilermando Almeida, Guilherme Lazzarini, Juliano Negri, Thiago H. Segreto, Ricardo V. Godoy, Marcelo Becker,
• Abstract summary: This paper presents a framework designed to enhance the grasping capabilities of quadrupeds equipped with arms.<n>We developed a pipeline within the Genesis simulation environment to generate a synthetic dataset of grasp attempts on common objects.<n>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.<n>We validated the complete framework on a four-legged robot.
• Score: 0.6533458718563319
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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