UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands

TL;DR

UniGrasp is a data-driven neural network model that predicts contact points for grasping objects, considering both object geometry and gripper attributes, enabling transfer across diverse robotic hands with high success rates.

Contribution

The paper introduces UniGrasp, a novel neural network architecture that generalizes grasp synthesis across different multifingered robotic hands by predicting contact points from object point clouds.

Findings

Over 90% valid contact points in Top10 predictions in simulation.

More than 90% successful grasps in real-world experiments for various hands.

Achieves high success rates on unseen robotic hands.

Abstract

To achieve a successful grasp, gripper attributes such as its geometry and kinematics play a role as important as the object geometry. The majority of previous work has focused on developing grasp methods that generalize over novel object geometry but are specific to a certain robot hand. We propose UniGrasp, an efficient data-driven grasp synthesis method that considers both the object geometry and gripper attributes as inputs. UniGrasp is based on a novel deep neural network architecture that selects sets of contact points from the input point cloud of the object. The proposed model is trained on a large dataset to produce contact points that are in force closure and reachable by the robot hand. By using contact points as output, we can transfer between a diverse set of multifingered robotic hands. Our model produces over 90% valid contact points in Top10 predictions in simulation and more than 90% successful grasps in real world experiments for various known two-fingered and three-fingered grippers. Our model also achieves 93%, 83% and 90% successful grasps in real world experiments for an unseen two-fingered gripper and two unseen multi-fingered anthropomorphic robotic hands.