지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2021.10
- 수록면
- 833 - 836 (4page)
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초록· 키워드
오류제보하기
When performing robotic assembly, a task should be conducted through force-based control such as impedance control. Using impedance control, it is possible to control the contact force by appropriately adjusting the impedance parameters. However, the impedance parameters should be set by the user because it is difficult to accurately recognize the dynamics of the contact environment, which takes a lot of time because it should be performed whenever the assembly task changes. Moreover, the parameters may not be optimal because it depends on the experience and skill level of the user. To this end, a reinforcement learning-based impedance parameter tuning method is proposed in this study. Since this method uses only the physics-based robotic simulation on the virtual environment, there is no risk of damaging the robots or parts and learning time can be significantly reduced. The proposed method was verified by assembling an HDMI connector with a tolerance of 0.03 mm. Impedance parameters were learned in the virtual environment and transferred to the real environment. Finally, it was confirmed that parameter tuning for impedance without the aid of the user is possible by using the proposed method.
목차
Abstract
1. INTRODUCTION
2. REINFORCEMENT LEARNING FOR IMPEDANCE PARAMETER TUNING
3. EXPERIMENTS
4. CONCLUSION
REFERENCES
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