Modular, Accessible, Sensorized Objects for Evaluating the Grasping and Manipulation Capabilities of Grippers and Hands
Geng Gao, Gal Gorjup, YU Ruo-bing, Patrick Jarvis, Minas Liarokapis
- Year
- 2020
- Citations
- 16
- Access
- Open access
Abstract
The human hand is Nature's most versatile and dexterous end-effector and it has been a source of inspiration for roboticists for over 50 years. Recently, significant industrial and research effort has been put into the development of dexterous robot hands and grippers. Such end-effectors offer robust grasping and dexterous, in-hand manipulation capabilities that increase the efficiency, precision, and adaptability of the overall robotic platform. This work focuses on the development of modular, sensorized objects that can facilitate benchmarking of the dexterity and performance of hands and grippers. The proposed objects aim to offer; a minimal, sufficiently diverse solution, efficient pose tracking, and accessibility. The object manufacturing instructions, 3D models, and assembly information are made publicly available through the creation of a corresponding repository.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002