Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics
This work demonstrates practical applicability of hierarchical inverse dynamics for humanoid robot balance control, addressing a domain-specific problem in robotics with incremental improvements.
The authors tackled the challenge of implementing hierarchical inverse dynamics controllers on a torque-controlled humanoid robot under real-world conditions like model inaccuracies and real-time constraints, achieving robust balance control even on one foot and efficient performance in tracking tasks.
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 disturbances, even when the robot is standing on only one foot. In a second experiment, a tracking task is evaluated to demonstrate the performance of the controller with more complicated hierarchies. Our results show that hierarchical inverse dynamics controllers can be used for feedback control of humanoid robots and that momentum-based balance control can be efficiently implemented on a real robot.