대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)
- 대한전기학회 1996년도 하계학술대회 논문집 B
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- Pages.1228-1230
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- 1996
Reinforcement 학습을 이용한 두발 로보트의 보행 자세 교정
Gait synthesis of a biped robot using reinforcement learning
초록
A neural network(NN) mechanism is proposed to modify the gait of a biped robot that walks on sloping surface using sensory inputs. The robot starts walking on a surface with no priori knowledge of the inclination of the surface. By accumulating experience during walking, the robot improves its walking gait and finally forms a gait that is adapted to the surface inclination. A neural controller is proposed to control the gait which has 72 reciprocally inhibited and excited neurons. PI control is used for position control, and the neurons are trained by a reinforcement learning mechanism. Experiments of static gait learning and pseudo dynamic learning are performed to show the validity of the proposed reinforcement learning mechanism.
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