Prioritized Optimal Control
This work addresses control challenges for humanoid robots and similar systems, but it appears incremental as it combines existing prioritized and optimal control approaches.
The paper tackles the problem of controlling highly redundant mechanical systems like humanoid robots by introducing strict priorities into multi-task optimal control, ensuring priority respect and avoiding numerical issues. In tests on a simulated 11-DOF robot, it was compared to prioritized control and optimal control, though no specific performance numbers are provided.
This paper presents a new technique to control highly redundant mechanical systems, such as humanoid robots. We take inspiration from two approaches. Prioritized control is a widespread multi-task technique in robotics and animation: tasks have strict priorities and they are satisfied only as long as they do not conflict with any higher-priority task. Optimal control instead formulates an optimization problem whose solution is either a feedback control policy or a feedforward trajectory of control inputs. We introduce strict priorities in multi-task optimal control problems, as an alternative to weighting task errors proportionally to their importance. This ensures the respect of the specified priorities, while avoiding numerical conditioning issues. We compared our approach with both prioritized control and optimal control with tests on a simulated robot with 11 degrees of freedom.