Dual-Arm Fabric Manipulation Learning With Grasp Pose Constraints

Dual-arm manipulation of fabrics is important and complex in the field of embodied intelligence for robots. Previous work has mostly focused on extracting the geometric features of fabrics, but has neglected the knowledge and experience constraints on grasp postures like humans, which greatly affects the success rate of operations. Therefore, this paper proposes a dual-arm manipulation method for fabrics based on imitation learning, including the study of an action encoding learning algorithm based on dynamic motion primitives (DMPs), the establishment of a dual-arm motion primitive library to provide prior knowledge for operation planning, the creation of a segment-based dual-arm grasp method based on prior knowledge, and the realization of extracting the optimal grasp posture from the implicit geometric features of messy fabrics. Experiments show that the success rate of this method in the fabric unfolding task is 85.0%, with an average of 5.0 operations, and the success rate in the fabric folding task is 96.7%, both of which are significantly better than the two baseline methods, SpeedFolding and FlingBot.