Three-Fingered Grasp Detection in Clutter With a Unified Grasping Representation

Robust grasp pose detection is crucial for robotic manipulation. Antipodal grasps are often inadequate for spatial objects. However, insufficient demonstrations and the higher complexity compared to parallel grippers make it challenging to implement learning-based robotic grasping with multi-fingered hands. To address these deficiencies, we propose a multi-modal grasp detection method tailored for three-fingered grippers. We utilize a simplified unified grasp representation, which can represent both precision and power grasping modes. Additionally, we introduce a pipeline to generate a multi-modal grasp dataset with a three-point contact grasp metric. We also present the concept of confness to quantify the probabilities of different grasp configurations within a given graspable area, guiding the predicted grasp poses to be concentrated around on centroidal region of objects. Furthermore, we develop QcMNet, an end-to-end network that is used to predict confness and infer grasp parameters. Experiments conducted with a Robotiq 3-Finger Adaptive Gripper, both in simulation and real-world scenarios, demonstrate that multi-modal grasping can increase the percent-cleared rate and enhance anti-disturbance capabilities in cluttered scenes. Note to Practitioners—This paper was motivated by the problem of finding the proper gripper configurations to ensure the stable grasping of objects across diverse tasks in industrial automation. Most existing approaches to robotic grasping detection utilize parallel grippers. However, it is insufficient to achieve stable grasping for spatial objects only with two frictional point contacts. This paper presents a learning-based grasp pose detection method tailored for three-fingered grippers, enabling robots to achieve a stable grasp on objects. We first propose a unified grasp representation that can represent different grasp modes, including precision and power grasp modes. Subsequently, a densely annotated dataset for three-fingered grippers is obtained by employing a robust grasp metric and an auto-annotation pipeline in simulation. This approach is designed to reduce labor consumption. The proposed pipeline not only enhances the efficiency of the data collection process but also ensures its diversity. Furthermore, we introduced a neural network to quantify the probabilities of different grasp modes within a graspable area and to infer grasp poses. Extensive physical robotic experiments validate the feasibility of this approach for grasping various objects. Significantly, most grasp poses concentrate around the centroid of objects, enhancing grasp stability. In future research, we will involve further optimizing the generated grasping candidates using local geometry information of objects. Additionally, we intend to generate online collision-free trajectories for robotic grasping to adapt to more complex automation environments.