Papers
11
Total Citations
136
H-Index
7
About
Asako Kanezaki is a leading researcher in robotics and artificial intelligence, specializing in autonomous navigation, object recognition, and multi-agent coordination. Her work bridges deep learning and robotic perception, enabling machines to operate intelligently in complex, dynamic environments. She developed GOSELO, a goal-directed navigation system using reactive neural networks (35 citations), and RotationNet, an unsupervised viewpoint estimation method for 3D object classification (20 citations). Kanezaki also advanced fast object detection for cluttered indoor settings using integral 3D feature tables (24 citations) and applied A3C reinforcement learning to motion planning in crowded spaces (15 citations). Her recent contributions include leveraging large language models for object-goal navigation (2025, 8 citations) and tactile estimation for stable robotic placement (2024, 7 citations). With over 130 total citations across her top works, Kanezaki’s research has significantly impacted robot autonomy, from incremental multi-view detection to deep reactive planning. Her innovative use of optical wireless communication for multi-agent visual coordination further underscores her role in shaping the future of intelligent, collaborative robotics.
Research Focus
Key Achievements
Top Papers
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- 4A3C Based Motion Learning for an Autonomous Mobile Robot in Crowds15 citations · 2019
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- 7Tactile Estimation of Extrinsic Contact Patch for Stable Placement7 citations · 2024
- 8Incremental multi-view object detection from a moving camera6 citations · 2021
- 9Deep Reactive Planning in Dynamic Environments5 citations · 2020
- 10Multi-Agent Visual Coordination Using Optical Wireless Communication4 citations · 2023