Trunk Pitch Oscillations for Joint Load Redistribution in Humans and Humanoid Robots
This work addresses the problem of gait planning for humanoid robots by providing insights into trunk motion, though it is incremental as it builds on existing models without broad validation.
The study tackled the challenge of creating natural running gaits for humanoid robots by investigating trunk oscillations using a TSLIP model with a virtual point target, showing that positioning the VP below the center of mass explains human-like forward trunk pitching and reduces leg loading but increases peak hip torque.
Creating natural-looking running gaits for humanoid robots is a complex task due to the underactuated degree of freedom in the trunk, which makes the motion planning and control difficult. The research on trunk movements in human locomotion is insufficient, and no formalism is known to transfer human motion patterns onto robots. Related work mostly focuses on the lower extremities, and simplifies the problem by stabilizing the trunk at a fixed angle. In contrast, humans display significant trunk motions that follow the natural dynamics of the gait. In this work, we use a spring-loaded inverted pendulum model with a trunk (TSLIP) together with a virtual point (VP) target to create trunk oscillations and investigate the impact of these movements. We analyze how the VP location and forward speed determine the direction and magnitude of the trunk oscillations. We show that positioning the VP below the center of mass (CoM) can explain the forward trunk pitching observed in human running. The VP below the CoM leads to a synergistic work between the hip and leg, reducing the leg loading. However, it comes at the cost of increased peak hip torque. Our results provide insights for leveraging the trunk motion to redistribute joint loads and potentially improve the energy efficiency in humanoid robots.