Saki Nakazawa
Papers
3
Total Citations
12
H-Index
2
About
Saki Nakazawa is a robotics researcher focused on advancing autonomous navigation for mobile robots, particularly in complex, crowded environments. Her work centers on developing adaptive navigation systems that dynamically switch between multiple control policies—including deep reinforcement learning—to optimize both safety and efficiency. Nakazawa’s key contributions include proposing novel methods that significantly improve collision rates and navigation performance in real-world crowd scenarios, as demonstrated in her 2023 papers, each garnering 5 citations. She has also pioneered an environment classification method using autoencoders to select appropriate crowd models for robot simulation, a 2024 work that addresses the critical challenge of matching simulation models to diverse real-world crowd dynamics. This research is vital for enabling robots to operate reliably in unpredictable human-filled spaces, such as shopping malls or train stations. Nakazawa’s work bridges the gap between simulation and reality, offering practical solutions for safer, more effective autonomous navigation—a key step toward widespread deployment of service and assistive robots.
Research Focus
Key Achievements
Top Papers
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