Multi-legged animals, such as insects and quadrupeds show stable locomotor patterns on various road surfaces and against disturbances with changing many locomotion parameters such as the stride period, duty ratio, and gait pattern according to the movement speed.
We are building mathematical models using robotics method and machine learning techniques how the nervous system achieves the learning control to realize such locomotor patterns and other various kinds of movements.
Legged animals change their gait pattern according to their locomotion speed, e.g., walking, trotting, and galloping. Our analysis by computer simulations and theoretical investigation explain that many characteristics of locomotor parameters observed in many legged animals, e.g., step length, stride period, and gait transition, are the results of the optimization based on energetic efficiency.
In order to reach our arm to a given target, there are infinite number of choices in the selection of arm joint trajectory. Many theoretical studies proposed criterion that explain the characteristics of the arm reaching trajectory, e.g., minimum jerk model, minimum torque change model, and minimum endpoint variance model.
We have shown that many characteristics of the arm reaching trajectory can also be explained by the minimum energetic cost model, the same criterion that explains the characteristics of locomotor patterns.
Evolution of altruistic behavior
According to Darwin’s theory of evolution, living things that can take more food than others and leave more offspring survive in that environment. However, it is difficult by this theory to explain the existence of altruistic behaviors that would benefit others, and discussions on this point have been ongoing since Darwin.
In our laboratory, we are examining by simulation experiments what kind of conditions make altruistic behaviors advantageous for survival.