A Robot Grasp Detection Method Based on Neural Architecture Search and Its Interpretability Analysis

Deep learning has become integral to robotics, particularly in tasks such as robotic grasping, where objects often exhibit diverse shapes, textures, and physical properties. In robotic grasping tasks, due to the diverse characteristics of the targets, frequent adjustments to the network architecture and parameters are required to avoid a decrease in model accuracy, which presents a significant challenge for non-experts. Neural Architecture Search (NAS) provides a compelling method through the automated generation of network architectures, enabling the discovery of models that achieve high accuracy through efficient search algorithms. Compared to manually designed networks, NAS methods can significantly reduce design costs, time expenditure, and improve model performance. However, such methods often involve complex topological connections, and these redundant structures can severely reduce computational efficiency. To overcome this challenge, this work puts forward a robotic grasp detection framework founded on NAS. The method automatically designs a lightweight network with high accuracy and low topological complexity, effectively adapting to the target object to generate the optimal grasp pose, thereby significantly improving the success rate of robotic grasping. Additionally, we use Class Activation Mapping (CAM) as an interpretability tool, which captures sensitive information during the perception process through visualized results. The searched model achieved competitive, and in some cases superior, performance on the Cornell and Jacquard public datasets, achieving accuracies of 98.3% and 96.8%, respectively, while sustaining a detection speed of 89 frames per second with only 0.41 million parameters. To further validate its effectiveness beyond benchmark evaluations, we conducted real-world grasping experiments on a UR5 robotic arm, where the model demonstrated reliable performance across diverse objects and high grasp success rates, thereby confirming its practical applicability in robotic manipulation tasks.