EfficientGrasp: A Unified Data-Efficient Learning to Grasp Method for Multi-fingered Robot Hands

Abstract

Autonomous grasping of novel objects that are previously unseen to a robot is an ongoing challenge in robotic manipulation. In the last decades, many approaches have been presented to address this problem for specific robot hands. The UniGrasp framework, introduced recently, has the ability to generalize to different types of robotic grippers; however, this method does not work on grippers with closed-loop constraints and is data-inefficient when applied to robot hands with multigrasp configurations. In this paper, we present EfficientGrasp, a generalized grasp synthesis and gripper control method that is independent of gripper model specifications. EfficientGrasp utilizes a gripper workspace feature rather than UniGrasp's gripper attribute inputs. This reduces memory use by 81.7% during training and makes it possible to generalize to more types of grippers, such as grippers with closed-loop constraints. The effectiveness of EfficientGrasp is evaluated by conducting object grasping experiments both in simulation and real-world; results show that the proposed method also outperforms UniGrasp when considering only grippers without closed-loop constraints. In these cases, EfficientGrasp shows 9.85% higher accuracy in generating contact points and 3.10% higher grasping success rate in simulation. The real-world experiments are conducted with a gripper with closed-loop constraints, which UniGrasp fails to handle while EfficientGrasp achieves a success rate of 83.3%. The main causes of grasping failures of the proposed method are analyzed, highlighting ways of enhancing grasp performance.

arXiv commentThis paper has been accepted for publication in IEEE Robotics and Automation Letters (RA-L), and for presentation at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)

References in corpus (5)

• Soft Actor-Critic Algorithms and Applications
• Data-Driven Grasp Synthesis - A Survey
• PointNetGPD: Detecting Grasp Configurations from Point Sets
• UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands
• Synthesizing Diverse and Physically Stable Grasps with Arbitrary Hand Structures using Differentiable Force Closure Estimator

Cited by in corpus (4)

• UGG: Unified Generative Grasping
• RESPRECT: Speeding-up Multi-fingered Grasping with Residual Reinforcement Learning
• A Grasp Pose is All You Need: Learning Multi-fingered Grasping with Deep Reinforcement Learning from Vision and Touch
• Dexterous grasp data augmentation based on grasp synthesis with fingertip workspace cloud and contact-aware sampling