Tadashi Onishi
Papers
1
Total Citations
5
H-Index
1
About
Tadashi Onishi is a robotics researcher whose work focuses on deep reactive planning and adaptive robot control in dynamic environments. His most cited paper, "Deep Reactive Planning in Dynamic Environments" (2020), introduces a novel end-to-end policy learning framework that enables robots to adjust their behavior in real time as environmental conditions change during task execution—a significant departure from traditional goal-conditioned reinforcement learning approaches, which struggle with such adaptability. This contribution addresses a critical gap in robotic autonomy, allowing for more resilient and responsive systems in unpredictable settings. With 5 citations, Onishi’s research is gaining recognition for its practical implications in fields like autonomous navigation and human-robot interaction. His work underscores a commitment to bridging the gap between theoretical reinforcement learning and real-world deployment, making him a promising voice in the advancement of intelligent, reactive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Deep Reactive Planning in Dynamic Environments5 citations · 2020