Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics
Alexander Herzog, Ludovic Righetti, Felix Grimminger, Peter, Pastor, Stefan Schaal

TL;DR
This paper demonstrates the successful implementation of hierarchical inverse dynamics controllers for humanoid robot balance, showing robustness and real-time performance despite model inaccuracies and disturbances.
Contribution
It presents an experimental evaluation of hierarchical inverse dynamics controllers on a humanoid robot, including a simplified optimization for real-time torque control.
Findings
Robust balance control on a humanoid robot using hierarchical inverse dynamics.
Efficient implementation of momentum-based balance controller in real-time.
Successful handling of disturbances and model uncertainties during experiments.
Abstract
Recently several hierarchical inverse dynamics controllers based on cascades of quadratic programs have been proposed for application on torque controlled robots. They have important theoretical benefits but have never been implemented on a torque controlled robot where model inaccuracies and real-time computation requirements can be problematic. In this contribution we present an experimental evaluation of these algorithms in the context of balance control for a humanoid robot. The presented experiments demonstrate the applicability of the approach under real robot conditions (i.e. model uncertainty, estimation errors, etc). We propose a simplification of the optimization problem that allows us to decrease computation time enough to implement it in a fast torque control loop. We implement a momentum-based balance controller which shows robust performance in face of unknown…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
