Stable, Concurrent Controller Composition for Multi-Objective Robotic Tasks
This work addresses the challenge of stable concurrent controller composition for robotic systems, offering a flexible framework for multi-task control, but it appears incremental as it builds on existing RMPflow methods.
The paper tackles the problem of designing stable controllers for multi-objective robotic tasks by decomposing them into subtasks and combining individual controllers using Riemannian Motion Policies (RMPs) and RMPflow, with a rigorous Control Lyapunov Function treatment to ensure stability. It validates the framework through numerical simulation and robotic implementation, though no concrete performance numbers are provided.
Robotic systems often need to consider multiple tasks concurrently. This challenge calls for controller synthesis algorithms that fulfill multiple control specifications while maintaining the stability of the overall system. In this paper, we decompose multi-objective tasks into subtasks, where individual subtask controllers are designed independently and then combined to generate the overall control policy. In particular, we adopt Riemannian Motion Policies (RMPs), a recently proposed controller structure in robotics, and, RMPflow, its associated computational framework for combining RMP controllers. We re-establish and extend the stability results of RMPflow through a rigorous Control Lyapunov Function (CLF) treatment. We then show that RMPflow can stably combine individually designed subtask controllers that satisfy certain CLF constraints. This new insight leads to an efficient CLF-based computational framework to generate stable controllers that consider all the subtasks simultaneously. Compared with the original usage of RMPflow, our framework provides users the flexibility to incorporate design heuristics through nominal controllers for the subtasks. We validate the proposed computational framework through numerical simulation and robotic implementation.