Multi-Object Grasping - Types and Taxonomy

This paper proposes 12 multi-object grasps (MOGs) types from a human and robot grasping data set. The grasp types are then analyzed and organized into a MOG taxonomy. This paper first presents three MOG data collection setups: a human finger tracking setup for multi-object grasping demonstrations, a real system with Barretthand, UR5e arm, and a MOG algorithm, a simulation system with the same settings as the real system. Then the paper describes a novel stochastic grasping routine designed based on a biased random walk to explore the robotic hand's configuration space for feasible MOGs. Based on obser-vations in both the human demonstrations and robotic MOG solutions, this paper proposes 12 MOG types in two groups: shape-based types and function-based types. The new MOG types are compared using six characteristics and then compiled into a taxonomy. This paper then introduces the observed MOG type combinations and shows examples of 16 different combinations.

Authors

• Yu Sun 0004
• Eliza Amatova
• Tianze Chen

Keywords

• Legged locomotion
• Automation
• Taxonomy
• Grasping
• Data collection
• Data models
• Planning
• Robotic Hand
• Biased Random Walk
• Multiple Objects
• Number Of Objects
• Simulation Setup
• Robotic Arm
• Tactile Sensor
• Human Hand
• Proximal Interphalangeal
• Track Types
• IceCube
• Finger Motion
• Multiple Fingers
• Funnel Shape