Fast Force-Closure Grasp Synthesis With Learning-Based Sampling

Anthropomorphic robotic hands have been widely investigated to dexterously manipulate objects because of their anatomical similarity to the human hand. However, the large dimension of configuration space challenges the real-time performance of existing grasp planning methods and drastically limits the application of anthropomorphic hands. In this letter, we propose a fast force-closure grasp synthesis (FFCGS) method for the anthropomorphic hand to efficiently grasp unknown objects. The FFCGS is implemented by using a signed distance field (SDF) as input. Firstly, a network that samples feasible 6D wrist poses is trained in an end-to-end fashion to reduce the dimension of search space. Furthermore, a fast optimization algorithm is presented to find finger configurations for force-closure precision grasp based on the differentiable Q-distance metric. We validate our method in both a simulated and a real-world environment. Experiment results show that the proposed FFCGS achieves a significantly improved performance in terms of time efficiency (5 times faster), grasp quality metrics, and success rate (5%-10% improvement) over benchmark methods. The outcomes of this study have great significance in promoting the motion planning of robot hand-arm systems and upper-limb prostheses.