| Budget Amount *help |
¥69,550,000 (Direct Cost: ¥53,500,000、Indirect Cost: ¥16,050,000)
Fiscal Year 2020: ¥13,650,000 (Direct Cost: ¥10,500,000、Indirect Cost: ¥3,150,000)
Fiscal Year 2019: ¥13,650,000 (Direct Cost: ¥10,500,000、Indirect Cost: ¥3,150,000)
Fiscal Year 2018: ¥13,650,000 (Direct Cost: ¥10,500,000、Indirect Cost: ¥3,150,000)
Fiscal Year 2017: ¥13,650,000 (Direct Cost: ¥10,500,000、Indirect Cost: ¥3,150,000)
Fiscal Year 2016: ¥14,950,000 (Direct Cost: ¥11,500,000、Indirect Cost: ¥3,450,000)
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| Outline of Final Research Achievements |
We introduced a brain-inspired motor learning framework for humanoid robot control. Specifically, we developed a computationally efficient Hierarchical Model Predictive Control (HMPC) method for real-time control of humanoid robots. Although MPC is a highly useful approach to deriving a policy for the control of nonlinear dynamical systems, its application to a robot having many degrees of freedom is still a challenging problem because MPC is quite computationally intensive. To cope with this issue, we developed the HMPC method that implements a three-layer hierarchical optimization procedure where a middle layer modulates pre-acquired movement patterns from captured human motions to guide the top-layer exploration and a lower layer generates reactive movements to quickly cope with external disturbances. We evaluated the proposed method on a simulated model and a real humanoid robot.
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