PUB - Publikationen an der Universität Bielefeld

This work proposes a new approach to robotic grasping exploiting conditional Wasserstein generative adversarial networks (WGANs), which output promising grasp candidates from depth image inputs. In contrast to discriminative models, the WGAN approach enables deliberative navigation in the set of feasible grasps and thus allows a smooth integration with other motion planning tools. We find that the training autonomously partitioned the space of feasible grasps into several regions corresponding to different grasp types. Each region forms a smooth grasp manifold with latent parameters corresponding to important grasp parameters like approach direction. We evaluate the model in simulation on the multi-fingered Shadow Robot hand, comparing it a) to a classical grasp planner for primitive geometric object shapes and b) to a state-of-the-art discriminative network model. The proposed generative model matches the grasp success rate of its trainer models and exhibits better generalization.