An Integrated Framework of Grasp Detection and Imitation Learning for Space Robotics Applications

Abstract Robots are key to expanding the scope of space applications. The end-to-end training for robot vision-based detection and precision operations is challenging owing to constraints such as extreme environments and high computational overhead. This study proposes a lightweight integrated framework for grasp detection and imitation learning, named GD-IL; it comprises a grasp detection algorithm based on manipulability and Gaussian mixture model (manipulability–GMM), and a grasp trajectory generation algorithm based on a two-stage robot imitation learning algorithm (TS-RIL). In the manipulability–GMM algorithm, we apply GMM clustering and ellipse regression to the object point cloud, propose two judgment criteria to generate multiple candidate grasp bounding boxes for the robot, and use manipulability as a metric for selecting the optimal grasp bounding box. The stages of the TS-RIL algorithm are grasp trajectory learning and robot pose optimization. In the first stage, the robot grasp trajectory is characterized using a second-order dynamic movement primitive model and Gaussian mixture regression (GMM). By adjusting the function form of the forcing term, the robot closely approximates the target-grasping trajectory. In the second stage, a robot pose optimization model is built based on the derived pose error formula and manipulability metric. This model allows the robot to adjust its configuration in real time while grasping, thereby effectively avoiding singularities. Finally, an algorithm verification platform is developed based on a Robot Operating System and a series of comparative experiments are conducted in real-world scenarios. The experimental results demonstrate that GD-IL significantly improves the effectiveness and robustness of grasp detection and trajectory imitation learning, outperforming existing state-of-the-art methods in execution efficiency, manipulability, and success rate.