Kenta Yoshizawa

Nara Institute of Science and Technology

Papers

Total Citations

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About

Kenta Yoshizawa is a robotics researcher whose work focuses on developing robust control systems for autonomous robots, particularly through the integration of feedback and feedforward policies. His key research areas include reinforcement learning (RL), model-free control, and fault-tolerant robotics. Yoshizawa’s major contribution lies in his optimization algorithm that enables robots to maintain stable performance even when sensing failures occur—a critical challenge for real-world deployment. By combining feedback (state-dependent) and feedforward (action-dependent) policies, his approach enhances robot resilience without relying on perfect sensor data. His most-cited paper, “Optimization algorithm for feedback and feedforward policies towards robot control robust to sensing failures” (2022), has garnered 4 citations, reflecting its niche but growing impact in the field of robust control. This work is notable for addressing a practical gap in traditional RL, which typically assumes reliable sensing. Yoshizawa’s research is particularly relevant for applications in hazardous environments or long-duration missions where sensor degradation is inevitable. His contributions advance the reliability of autonomous systems, making him a promising figure in the intersection of control theory and robotics.