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About

Dotaro Usui is a robotics researcher whose work focuses on enabling robots to perceive and interact with their environment through tactile sensing, particularly when vision is unreliable. His core research areas include object pose estimation, deformable object manipulation, and state estimation using particle filters. Usui's major contributions center on developing novel algorithms that allow robots to estimate the position and orientation of objects—including flexible containers and bags—by leveraging force-torque sensors and soft tactile sensors. He pioneered the Manifold Particle Filter and its extension, C-MPF, which can handle continuous, multidimensional tactile observations for more accurate pose estimation. His work on optimal action selection using information gain has practical implications for industrial automation, such as picking packed circuit boards or bags. With over 13 citations across his most-cited papers, Usui's research is gaining traction in the robotics community. Notably, his 2024 study on estimating the aperture of a bag using force-torque sensors demonstrates a creative application of particle filtering to deformable objects, highlighting his ability to solve real-world manipulation challenges where traditional vision-based methods fall short.