A Surprisingly Efficient Representation for Multi-Finger Grasping

The problem of grasping objects using a multi-finger hand has received significant attention in recent years. However, it remains challenging to handle a large number of unfamiliar objects in real and cluttered environments. In this work, we propose a representation that can be effectively mapped to the multi-finger grasp space. Based on this representation, we develop a simple decision model that generates accurate grasp quality scores for different multi-finger grasp poses using only hundreds to thousands of training samples. We demonstrate that our representation performs well on a real robot and achieves a success rate of 78. 64% after training with only 500 real-world grasp attempts and 87% with 4500 grasp attempts. Additionally, we achieve a success rate of 84. 51% in a dynamic human-robot handover scenario using a multi-finger hand.

Authors

• Hengxu Yan
• Haoshu Fang
• Cewu Lu

Keywords

• Training
• Adaptation models
• Accuracy
• Supervised learning
• Training data
• Grasping
• Handover
• Decision Model
• Real Robot
• Degrees Of Freedom
• Training Dataset
• Representative Model
• Point Cloud
• Simulation Environment
• Robotic Arm
• Human Hand
• Collision Detection
• Intermediate Representation
• Dynamic Scenes
• Temporal Continuity
• Static Scenes
• In-plane Rotation