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

arXiv: https://arxiv.org/abs/1911.13233 · HTML: https://mlsanity.com/paper/1911.13233/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [XBG: End-to-end Imitation Learning for Autonomous Behaviour in Human-Robot Interaction and Collaboration](https://mlsanity.com/paper/2406.15833/) (2024)
- [Learning to Evaluate Autonomous Behaviour in Human-Robot Interaction](https://mlsanity.com/paper/2507.06404/) (2025)
- [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 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.