Humanoid full-body manipulation planning with multiple initial guesses and key postures

Abstract

We present an optimization method to solve coupled redundant inverse kinematics problems and generate trajectories for humanoid robot full-body manipulation. The basic idea of our algorithm is to divide a manipulation task into a series of key postures, generate multiple diverse initial guesses for each key posture, and use optimization to find inverse kinematics solutions based on these initial guesses. We then find an optimal series of key postures and form a continuous trajectory. Our approach is implemented in a Gazebo simulation using the Atlas humanoid robot from Boston Dynamics.