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About

Daisuke Kato is a robotics researcher whose work focuses on enabling robots to perceive and manipulate objects through touch, particularly when vision alone is insufficient. His core research areas include tactile sensing, object pose estimation, and state estimation for deformable objects using probabilistic methods. Kato’s major contributions center on extending the Manifold Particle Filter (MPF) framework to handle continuous, multidimensional tactile observations, allowing robots to estimate object pose through iterative contact with soft tactile sensors. He has also pioneered information gain-based optimal action selection for state estimation of deformable objects, such as bags containing unknown contents, using force-torque sensor data. His work on efficient sample collection to construct observation models further advances practical deployment of contact-based pose estimation. With papers published in 2023 and 2024, Kato’s research is gaining traction, accumulating citations that reflect growing interest in tactile sensing as a robust alternative to vision in cluttered or occluded environments. His innovative approaches—like the C-MPF algorithm and discrete state discrimination for container aperture estimation—are helping to build the foundation for more dexterous, touch-aware robotic manipulation systems.