Motor control insights on walking planner and its stability

Abstract

The application of biomechanics and motor control models in the control of bipedal robots (humanoids and exoskeletons) has revealed limitations of our understanding of human locomotion. A recently proposed model uses the potential energy for bipedal structures to model the bipedal dynamics, allowing us to predict the system dynamics from its kinematics. This work proposes a task-space planner for human-like straight locomotion that targets application in rehabilitation robotics and computational neuroscience. The proposed architecture is based on the potential energy model and employs locomotor strategies from human data. The model generates Centre of Mass (CoM) trajectories, foot swing trajectories and the Base of Support (BoS) over time. The data show that the proposed architecture can generate behaviour in line with human walking strategies for both the CoM and the foot swing. Despite the CoM vertical trajectory not being as smooth as a human trajectory, the proposed model significantly reduces the error in the estimation of the CoM vertical trajectory compared to the inverted pendulum models. The proposed model can also assess stability based on the body kinematics embedding currently used in clinical practice. However, the model also implies a shift in the interpretation of the spatiotemporal parameters of the gait, which are now determined by the conditions for the equilibrium and not vice versa. In other words, locomotion is a dynamic reaching where the motor primitives are also determined by gravity.