1. Wrist-driven passive grasping: interaction-based trajectory adaption with a compliant anthropomorphic hand
- Author
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Gilday, Kieran, Hughes, Josie, Iida, Fumiya, Gilday, Kieran [0000-0002-8264-1535], Hughes, Josephine [0000-0001-8410-3565], Iida, Fumiya [0000-0001-9246-7190], Apollo - University of Cambridge Repository, and Hughes, Josie [0000-0001-8410-3565]
- Subjects
New approaches ,Paper ,0209 industrial biotechnology ,Computer science ,Passive objects ,Biophysics ,02 engineering and technology ,Degrees of freedom (mechanics) ,Wrist ,Biochemistry ,Trajectories ,wrist control ,Task (project management) ,Motion ,020901 industrial engineering & automation ,medicine ,Humans ,Computer vision ,human ,Range of Motion, Articular ,Musculo-skeletal ,Engineering (miscellaneous) ,Hand Strength ,business.industry ,Robot hand ,compliant interactions ,Anthropomorphic robots ,Hand ,021001 nanoscience & nanotechnology ,Object (computer science) ,Passive dynamics ,medicine.anatomical_structure ,passive grasping ,Trajectory ,Molecular Medicine ,Parameterized ,Wrist motions ,Artificial intelligence ,0210 nano-technology ,business ,joint characteristics and functions ,anthropomorphic hand ,Object size ,Biotechnology - Abstract
The structure of the human musculo-skeletal systems shows complex passive dynamic properties, critical for adaptive grasping and motions. Through wrist and arm actuation, these passive dynamic properties can be exploited to achieve nuanced and diverse environment interactions. We have developed a passive anthropomorphic robot hand that shows complex passive dynamics. We require arm/wrist control with the ability to exploit these. Due to the soft hand structures and high degrees of freedom during passive-object interactions, bespoke generation of wrist trajectories is challenging. We propose a new approach, which takes existing wrist trajectories and adapts them to changes in the environment, through analysis and classification of the interactions. By analysing the interactions between the passive hand and object, the required wrist motions to achieve them can be mapped back to control of the hand. This allows the creation of trajectories which are parameterized by object size or task. This approach shows up to 86% improvement in grasping success rate with a passive hand for object size changes up to +/-50%., EPSRC Doctoral Training Programme Studentship and Arm Ltd (RG99055).
- Published
- 2021
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