| Budget Amount *help |
¥16,380,000 (Direct Cost: ¥12,600,000、Indirect Cost: ¥3,780,000)
Fiscal Year 2020: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
Fiscal Year 2019: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2018: ¥9,230,000 (Direct Cost: ¥7,100,000、Indirect Cost: ¥2,130,000)
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| Outline of Final Research Achievements |
To elucidate the mechanism in environmental model-independent adaptive learning, the mechanism of motion pattern generation was investigated in detail using model-free deep reinforcement learning. Although mathematical optimization calculations require a mathematical model of the environment and the body a priori, none have been treated so far as motor learning in an unknown physical environment. We investigated for potential computational guidelines in multi-joint gait. We examined the level of emergence of motor synergy in walking movements that changed as learning progressed and found that the synergy emergence was highly correlated with the performance per energy. It implies that motor synergy is employed as a necessary condition for improving performance per energy.
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