Quadrupedal Gait Generation Based on Human Feeling for Animal Type Robot 265 Quadrupedal Gait Generation Based on Human Feeling for Animal Type Robot

Animals have for long been recognized as being a positive force in healing processes (Baun et al., 1984). In recent years, animal-assisted therapy (AAT), which makes use of the healing effects of animals has attracted attention (Fine, 2006). Examples of the expected results of this type of therapy are buffering actions for stress, improvement of sociability and shortening of the medical treatment period through mental healing. Thus, the introduction of AAT is being considered in hospitals and health facilities. However, it is difficult to employ AAT in such facilities because of the risks of the spread of infection from animals to patients and the necessity of proper animal training. Robot-assisted therapy (RAT), in which robots resembling animals are used instead of real animals, is important for patient safety (Shibata et al., 2005). Pet robots resembling various animals, such as the dog robot “AIBO”, seal robot “Paro”, etc., are used in this type of therapy. Banks et al. reported no difference between the effectiveness of a living dog and an AIBO robotic dog in reducing loneliness (Banks et al., 2008). Shibata et al. applied a mental commit robot, Paro, to RAT, and they verified that the interaction with Paro has psychological, physiological and social effects on people (Shibata et al., 2004; Wada et al., 2005). In these applications, it is important that the robot imitates the motions of living animal, especially essential motions, such as walking, running, etc. However, it is difficult for the robot to walk and run like an animal because it is affected by various types of dynamic noise in the real world, in contrast to the ideal world. In recent years, many researchers have studied gait generation methods for various types of robots (Estremera & Santos, 2005; Kimura et al., 2005). A legged robot in the real world will have nDOF (degrees of freedom) for movement, and it is difficult to solve the optimization problem in n-dimensional continuous state/action space to generate an adequate gait (Kimura et al., 2001). Therefore, evolutionary approaches, such as use of fuzzy logic, genetic algorithms, neural networks, or various hybrid systems, are employed for gait learning and parameter optimization (Inada & Ishii, 2003; Son et al., 2002). For example, Chernova et al. generated fast forward gaits using an evolutionary approach for quadruped robots (Chernova & Velosa, 2004). However, these gait generation methods for legged robots did not evaluate the degree to which the robot's movement approximated that of a living animal, because they were not designed for enhancement of the effects of RAT. 16