MagicHand: Context-Aware Dexterous Grasping Using an Anthropomorphic Robotic Hand

Understanding of characteristics of objects such as fragility, rigidity, texture and dimensions facilitates and innovates robotic grasping. In this paper, we propose a context- aware anthropomorphic robotic hand (MagicHand) grasping system which is able to gather various information about its target object and generate grasping strategies based on the perceived information. In this work, NIR spectra of target objects are perceived to recognize materials on a molecular level and RGB-D images are collected to estimate dimensions of the objects. We selected six most used grasping poses and our system is able to decide the most suitable grasp strategies based on the characteristics of an object. Through multiple experiments, the performance of the MagicHand system is demonstrated.

I. Introduction

Context-aware systems are designed to collect useful information from their environments and adapt their behaviors based on the information [1]. This idea is widely used in robot cooperation [2], patients assistant, object tracking [6], swarm robotics system [7] etc. Dexterous manipulation is able to aid the robot to accomplish complicate grasp tasks, especially for those tasks which require precise manipulation and dangerous or infeasible to humans, such as planetary exploration, underwater salvage, and objects retrieval under dangerous environments. It is a complex decision-making process that depends on the task to be performed and the physical characteristics of the object such as fragility, rigidity, texture, shape and dimensions.