# A Control Architecture with Online Predictive Planning for Position and Torque Controlled Walking of Humanoid Robots

arXiv: https://arxiv.org/abs/1807.05395 · HTML: https://mlsanity.com/paper/1807.05395/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 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)
- [Know your limits! Optimize the robot's behavior through self-awareness](https://mlsanity.com/paper/2409.10308/) (2024)

## Abstract (arXiv)

A common approach to the generation of walking patterns for humanoid robots consists in adopting a layered control architecture. This paper proposes an architecture composed of three nested control loops. The outer loop exploits a robot kinematic model to plan the footstep positions. In the mid layer, a predictive controller generates a Center of Mass trajectory according to the well-known table-cart model. Through a whole-body inverse kinematics algorithm, we can define joint references for position controlled walking. The outcomes of these two loops are then interpreted as inputs of a stack-of-task QP-based torque controller, which represents the inner loop of the presented control architecture. This resulting architecture allows the robot to walk also in torque control, guaranteeing higher level of compliance. Real world experiments have been carried on the humanoid robot iCub.