Generating Grasp Poses for a HighDOF Gripper Using Neural Networks We present a learningbased method for representing grasp poses of a highDOF hand using neural networks. Due to redundancy in such highDOF grippers, there exists a large number of equally effective grasp poses for a given target object, making it difficult for the neural network to find consistent grasp poses. We resolve this ambiguity by generating an augmented dataset that covers many possible grasps for each target object and train our neural networks using a consistency loss function to identify a onetoone mapping from objects to grasp poses. We further enhance the quality of neuralnetworkpredicted grasp poses using a collision loss function to avoid penetrations. We use an object dataset that combines the BigBIRD Database, the KIT Database, the YCB Database, and the Grasp Dataset to show that our method can generate highDOF grasp poses with higher accuracy than supervised learning baselines. The quality of the grasp poses is on par with the groundtruth poses in the dataset. In addition, our method is robust and can handle noisy object models such as those constructed from multiview depth images, allowing our method to be implemented on a 25DOF Shadow Hand hardware platform.

A Cascaded Deep Learning Framework for Realtime and Robust Grasp Planning * Robotic grasping has been regarded as an essential way for robots to interact with the environment, which makes it a research hotspot. To our knowledge, current work on grasp planning could rarely take both robustness and time efficiency into account. To solve this problem, we propose a cascaded deep learning framework, which consists of a regression CNN and a refined CNN. Taking depth image as input, the first network regresses an optimal grasp region. The second network samples and sorts grasp candidates in the region and finally outputs a series of grasp poses with high quality. This architecture ensures realtime performance and acquisition of adequate candidates synchronously, which is especially appropriate for unstructured environment manipulation. We used Gazebo simulation and DART physics engines to build a grasp data generation environment. Comparing with manually annotating or robot collecting, our method makes data generation less expensive, more dynamic, and easier to generalize into the real world. The method proposed is evaluated in both simulation and physical experiments, verifying that our method is able to find multiple robuster grasp poses at a faster rate than the state of the art.