# A Benchmarking of DCM Based Architectures for Position and Velocity Controlled Walking of Humanoid Robots

arXiv: https://arxiv.org/abs/1809.02167 · HTML: https://mlsanity.com/paper/1809.02167/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2018.

## Built on by (most cited first, 2 of 2)

- [On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning](https://mlsanity.com/paper/2104.14534/) (2021)
- [Stabilizing Humanoid Robot Trajectory Generation via Physics-Informed Learning and Control-Informed Steering](https://mlsanity.com/paper/2509.24697/) (2025)

## Abstract (arXiv)

This paper contributes towards the development and comparison of Divergent-Component-of-Motion (DCM) based control architectures for humanoid robot locomotion. More precisely, we present and compare several DCM based implementations of a three layer control architecture. From top to bottom, these three layers are here called: trajectory optimization, simplified model control, and whole-body QP control. All layers use the DCM concept to generate references for the layer below. For the simplified model control layer, we present and compare both instantaneous and Receding Horizon Control controllers. For the whole-body QP control layer, we present and compare controllers for position and velocity control robots. Experimental results are carried out on the one-meter tall iCub humanoid robot. We show which implementation of the above control architecture allows the robot to achieve a walking velocity of 0.41 meters per second.