DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Hwangbo, Jemin | - |
dc.contributor.advisor | 황보제민 | - |
dc.contributor.author | Ji, Gwanghyeon | - |
dc.date.accessioned | 2023-06-22T19:30:56Z | - |
dc.date.available | 2023-06-22T19:30:56Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1032285&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/308136 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 기계공학과, 2023.2,[iii, 25 p. :] | - |
dc.description.abstract | In this paper, we propose a locomotion training framework where a control policy and a state estimator are trained concurrently. The framework consists of a policy network which outputs the desired joint positions and a state estimation network which outputs estimates of the robot’s states such as the base linear velocity, foot height, and contact probability. We exploit a fast simulation environment to train the networks and the trained networks are transferred to the real robot. The trained policy and state estimator are capable of traversing diverse terrains such as a hill, slippery plate, and bumpy road. We also demonstrate that the learned policy can run at up to 3.75 m/s on normal flat ground and 3.54 m/s on a slippery plate with the coefficient of friction of 0.22. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Legged robots▼aReinforcement learning▼aHigh-speed locomotion | - |
dc.subject | 보행 로봇▼a강화 학습▼a고속 보행 | - |
dc.title | Concurrent training of a control policy and a state estimator for dynamic and robust legged locomotion | - |
dc.title.alternative | 사족로봇의 고속보행제어를 위한 정책 및 상태추정 동시학습 기법 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :기계공학과, | - |
dc.contributor.alternativeauthor | 지광현 | - |
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