Ryo Terasawa
Papers
4
Total Citations
56
H-Index
4
About
Ryo Terasawa is a leading researcher in humanoid robotics, specializing in motion planning, dynamic whole-body control, and supervised autonomy for disaster response. His work bridges the gap between simulation and real-world robotic performance, particularly in high-speed, dynamic tasks. His most cited paper (26 citations) introduces a 3D-CNN-based heuristic planner that dramatically accelerates sampling-based motion planning, enabling faster collision-free paths for robotic manipulation. Terasawa also pioneered methods for generating dynamic sports motions, such as a tennis swing on a humanoid robot (14 citations), by combining offline motion planning with online optimization under physical and balance constraints. His contributions to the DARPA Robotics Challenge were pivotal: he developed a recognition-guided teleoperation driving system (9 citations) that allowed humanoids to estimate vehicle paths, and a unified UI system (7 citations) for supervised autonomy over narrow, unreliable networks, enabling massive data visualization. These systems were critical for Team NEDO-JSK’s success in the competition. Terasawa’s work demonstrates how deep learning and optimization can elevate humanoid robots from static tasks to agile, real-world operation, making him a key figure in advancing robot autonomy for hazardous environments.
Research Focus
Key Achievements
Top Papers
- 13D-CNN Based Heuristic Guided Task-Space Planner for Faster Motion Planning26 citations · 2020
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