Ikki Kishida
Papers
1
Total Citations
5
H-Index
1
About
Ikki Kishida is a researcher advancing the frontiers of robotic perception and lifelong learning, with a focus on enabling robots to operate intelligently in dynamic home environments. His key research areas include continual learning, open-set domain adaptation, and object recognition for autonomous systems. Kishida’s most-cited work, “Object Recognition with Continual Open Set Domain Adaptation for Home Robot” (2021, 5 citations), tackles a critical challenge: how robots can recognize previously seen objects while ignoring novel, irrelevant ones during real-world tasks like object searching. This contribution bridges the gap between static training and adaptive, open-world deployment, addressing the problem of distribution shift and unknown categories—a vital step toward human-like robotic cognition. While his citation count is modest, his work is foundational for researchers exploring robust, lifelong perception in robotics. Kishida’s research underscores the importance of adaptability and safety in autonomous systems, making him a notable voice in the growing field of continual learning for embodied AI. His efforts inspire students and engineers to rethink how robots learn and interact with the ever-changing physical world.
Research Focus
Key Achievements
Top Papers
- 1