A Benchmarking of DCM Based Architectures for Position, Velocity and Torque Controlled Humanoid Robots

TL;DR

This paper benchmarks control architectures based on Divergent Component of Motion for humanoid robots, comparing different control layers and demonstrating a new highest walking velocity for the iCub robot.

Contribution

It introduces a comparative analysis of DCM-based control architectures, including two implementations of the simplified model control layer, and reports the highest walking velocity achieved by iCub.

Findings

Receding Horizon controller improves stability.

One architecture achieves 0.3372 m/s walking velocity.

Comparison highlights effectiveness of different control modes.

Abstract

This paper contributes towards the benchmarking of control architectures for bipedal robot locomotion. It considers architectures that are based on the Divergent Component of Motion (DCM) and composed of three main layers: trajectory optimization, simplified model control, and whole-body QP control layer. While the first two layers use simplified robot models, the whole-body QP control layer uses a complete robot model to produce either desired positions, velocities, or torques inputs at the joint-level. This paper then compares two implementations of the simplified model control layer, which are tested with position, velocity, and torque control modes for the whole-body QP control layer. In particular, both an instantaneous and a Receding Horizon controller are presented for the simplified model control layer. We show also that one of the proposed architectures allows the humanoid robot iCub to achieve a forward walking velocity of 0.3372 meters per second, which is the highest walking velocity achieved by the iCub robot.