2019 Fiscal Year Research-status Report

Understand human tool-use in motion and contact for robotic tooling

Research Project

Project/Area Number	18K18130
Research Institution	NTT Communication Science Laboratories
Principal Investigator	高木敦士日本電信電話株式会社NTTコミュニケーション科学基礎研究所, 人間情報研究部, 特別研究員 (70802362)
Project Period (FY)	2018-04-01 – 2021-03-31
Keywords	grasp force / endpoint stiffness / precision control
Outline of Annual Research Achievements	In FY2018, we reported the development of a new grasp force metholodogy that measured what appeared to be adaptations in endpoint stiffness. This year in FY2019, we validated the grasp force methodology by measuring its relationship with the endpoint stiffness magnitude. We found a linear correlation between these two quantities, and have published the results in an international journal. Furthermore, to demonstrate that the grasp force correlates with the control of movement precision, we examined how the grasp changed in two separate experiments where precision is known to be controlled. In both experiments, we found an adaptation in the grasp force similar to what was reported in the literature (concerning stiffness adaptation). This has also been published in an international journal.
Current Status of Research Progress	Current Status of Research Progress 2: Research has progressed on the whole more than it was originally planned. Reason The research in FY2019 is progressing smoothly. We were able to validate the grasp force methodology to gain a deeper understanding of how movement precision is controlled during interaction and during movement. This resulted in two international journal publications.
Strategy for Future Research Activity	We have now gathered sufficient data from multiple motor tasks to model the control of movement precision. We aim to understand on a deep level the control mechanism utilized by the brain to control the interaction force exerted by the arm. We also want to understand the adaptation process of precision control to gain an insight into how humans learn an insertion (tooling) task.
Causes of Carryover	Expenses were somewhat lower than expected in FY2019. The amount remaining will be used to purchase a three-axis force sensor in FY2020.