| Budget Amount *help |
¥17,420,000 (Direct Cost: ¥13,400,000、Indirect Cost: ¥4,020,000)
Fiscal Year 2023: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2022: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2021: ¥11,830,000 (Direct Cost: ¥9,100,000、Indirect Cost: ¥2,730,000)
Fiscal Year 2020: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
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| Outline of Final Research Achievements |
This study aims to develop learning and control techniques for safe physical interaction between robots and humans. In relation to this scenario, four technical results have been obtained mainly: i) a well-formed latent space extraction technique based on Tsallis statistics; ii) a smoothing technique for reinforcement learning action; iii) a new theory of Sim-to-Real as multiobjective reinforcement learning; and iv) a stochastic gradient descent method robust to noise and outliers. In addition, two applications have been conducted mainly: i) analysis of periodic motion of indirect physical human-robot interaction; and ii) footstep planning of bipeds with discrete changes of contact states.
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