Learning and Sensor Information Processing for Intelligent Robots
In this laboratory, we are conducting research on recognition, motion generation and control, and sensor information processing (image processing) for intelligent robots. The basic techniques for our research are
Image processing : Technology to realize the function of the human eye with computers
Control and learning : Technology to move or maintain an object in a desired state, and technology to deal with an uncertain or unknown object based on experienced information
Mechatronics: Technology that integrates mechanical and electronic engineering and uses computers to control machines
Development of a motor learning model that can explain the reuse of partially motor acquired skills in the past.
We are developing a motor learning model that explains the process of reusing partial dynamics in previously acquired controllers by introducing a mechanism, "translation estimation between mappings", into a motor learning model that estimates dependencies between various senses.
Automatic Generation of Control Rules for Musculoskeletal Arm Systems Based on the Estimation of Dependencies between Heterogeneous Sensors
We are developing a method for motion control that includes the process of identifying dependencies between different sensors and estimating "which sensor information can be used to achieve the desired control", considering a system that can perform redundant and multimodal sensing.