Autonomous learning-free grasping and robot-to-robot handover of unknown objects

Abstract

In this paper, we propose a learning-free approach for an autonomous robotic system to grasp, hand over, and regrasp previously unseen objects. The proposed framework includes two main components: a novel grasping detector to predict grasping poses directly from the point cloud and a reachability-aware handover planner to select the exchange pose and grasping poses for two robots. In the grasping detection stage, multiple superquadrics are first recovered at different positions within the object, representing the local geometric feature of the object. Our algorithm then exploits the tri-symmetry feature of superquadrics and synthesizes a list of antipodal grasps from each recovered superquadric. An evaluation model is designed to assess and quantify the quality of each grasp candidate. In the handover planning stage, the planner first selects grasping candidates that have high scores and a larger number of collision-free partners. Then the exchange location is computed by utilizing two signed distance fields (SDF) which model the reachability space for the pair of two robots. To evaluate the performance of the proposed method, we first run experiments on isolated and packed scenes to corroborate the effectiveness of our grasping detection method. Then the handover experiments are conducted on a dual-arm system with two 7 degrees of freedom (DoF) manipulators. The results indicate that our method shows better performance compared with the state-of-the-art, without the need for large amounts of training.