Realization of a Human-like Gait for a Bipedal Robot Based on Gait Analysis

Abstract

There are many studies analyzing human motion. However, we do not yet fully understand the mechanisms of our own bodies. We believe that mimicking human motion and function using a robot will help us to deepen our understanding of humans. Therefore, we focus on the characteristics of the human gait, and the goal is to realize a human-like bipedal gait that lands on its heels and takes off from its toes. In this study, we focus on kinematic synergy (planar covariation) in the lower limbs as a characteristic gait seen in humans. Planar covariation is that elevation angles at the thigh, shank, and foot in the sagittal plane are plotted on one plane when the angular data are plotted on the three axes. We propose this feature as a reward for reinforcement learning. By introducing this reward, the bipedal robot achieved a human-like bipedal gait in which the robot lands on its heels and takes off from its toes. We also compared the learning results with those obtained when this feature was not used. The results suggest that planar covariation is one factor that characterizes a human-like gait.