Sparse Optimization of Contact Forces for Balancing Control of Multi-Legged Humanoids

Abstract

Multi-legged humanoid platforms present an inherent redundancy in the number of end-effectors required to perform interaction tasks, such as balancing and manipulation. The most relevant possibility opened up by end-effector redundancy consists in using a subset of the available end-effectors to perform a primary task, while employing the remaining end-effectors to perform a secondary tasks. As a consequence, it necessarily requires a methodology to automatically find the smallest set of end-effectors required to perform a primary task. For the balancing control of a torque-controlled humanoid, this is equivalent to finding a sparse solution of a contact force distribution problem. To this end, two different sparse optimization approaches are presented and extensively discussed in this work. The effectiveness of the proposed approaches has been validated on a simulated model of the CENTAURO robot developed at the Istituto Italiano di Tecnologia.