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

Quadruped robots have emerged as highly efficient and versatile platforms, excelling in navigating complex and unstructured terrains where traditional wheeled robots might fail. Equipping these robots with manipulator arms unlocks the advanced capability of loco-manipulation to perform complex physical interaction tasks in areas ranging from industrial automation to search-and-rescue missions. However, achieving precise and adaptable grasping in such dynamic scenarios remains a significant challenge, often hindered by the need for extensive real-world calibration and pre-programmed grasp configurations. This paper introduces a deep learning framework designed to enhance the grasping capabilities of quadrupeds equipped with arms, focusing on improved 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.

A Synthetic Dataset for Manometry Recognition in Robotic Applications

This work addresses the challenges of data scarcity and high acquisition costs for training robust object detection models in complex industrial environments, such as offshore oil platforms. The practical and economic barriers to collecting real-world data in these hazardous settings often hamper the development of autonomous inspection systems. To overcome this, in this work we propose and validate a hybrid data synthesis pipeline that combines procedural rendering with AI-driven video generation. Our methodology leverages BlenderProc to create photorealistic images with precise annotations and controlled domain randomization, and integrates NVIDIA's Cosmos-Predict2 world-foundation model to synthesize physically plausible video sequences with temporal diversity, capturing rare viewpoints and adverse conditions. We demonstrate that a YOLO-based detection network trained on a composite dataset, blending real images with our synthetic data, achieves superior performance compared to models trained exclusively on real-world data. Notably, a 1:1 mixture of real and synthetic data yielded the highest accuracy, surpassing the real-only baseline. These findings highlight the viability of a synthetic-first approach as an efficient, cost-effective, and safe alternative for developing reliable perception systems in safety-critical and resource-constrained industrial applications.

Improving Failure Prediction in Aircraft Fastener Assembly Using Synthetic Data in Imbalanced Datasets

Automating aircraft manufacturing still relies heavily on human labor due to the complexity of the assembly processes and customization requirements. One key challenge is achieving precise positioning, especially for large aircraft structures, where errors can lead to substantial maintenance costs or part rejection. Existing solutions often require costly hardware or lack flexibility. Used in aircraft by the thousands, threaded fasteners, e.g., screws, bolts, and collars, are traditionally executed by fixed-base robots and usually have problems in being deployed in the mentioned manufacturing sites. This paper emphasizes the importance of error detection and classification for efficient and safe assembly of threaded fasteners, especially aeronautical collars. Safe assembly of threaded fasteners is paramount since acquiring sufficient data for training deep learning models poses challenges due to the rarity of failure cases and imbalanced datasets. The paper addresses this by proposing techniques like class weighting and data augmentation, specifically tailored for temporal series data, to improve classification performance. Furthermore, the paper introduces a novel problem-modeling approach, emphasizing metrics relevant to collar assembly rather than solely focusing on accuracy. This tailored approach enhances the models' capability to handle the challenges of threaded fastener assembly effectively.

A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation

Soybean and cotton are major drivers of many countries' agricultural sectors, offering substantial economic returns but also facing persistent challenges from volunteer plants and weeds that hamper sustainable management. Effectively controlling volunteer plants and weeds demands advanced recognition strategies that can identify these amidst complex crop canopies. While deep learning methods have demonstrated promising results for leaf-level detection and segmentation, existing datasets often fail to capture the complexity of real-world agricultural fields. To address this, we collected 640 high-resolution images from a commercial farm spanning multiple growth stages, weed pressures, and lighting variations. Each image is annotated at the leaf-instance level, with 7,221 soybean and 5,190 cotton leaves labeled via bounding boxes and segmentation masks, capturing overlapping foliage, small leaf size, and morphological similarities. We validate this dataset using YOLOv11, demonstrating state-of-the-art performance in accurately identifying and segmenting overlapping foliage. Our publicly available dataset supports advanced applications such as selective herbicide spraying and pest monitoring and can foster more robust, data-driven strategies for soybean-cotton management.