Robust Sampling-Based Control of Mobile Manipulators for Interaction With Articulated Objects

In this article, we investigate and deploy sampling-based control techniques for the challenging task of the mobile manipulation of articulated objects. By their nature, manipulation tasks necessitate environment interactions, which require the handling of nondifferentiable switching contact dynamics. These dynamics represent a strong limitation for traditional gradient-based optimization methods, such as model-predictive control and differential dynamic programming, which often rely on heuristics for trajectory generation. Sampling-based techniques alleviate these constraints but do not ensure robots' stability and input/state constraints either. On the other hand, real-world applications in human environments require safety and robustness to unexpected events. For this reason, we propose a novel framework for safe robotic manipulation of movable articulated objects. The framework combines sampling-based control together with control barrier functions and passivity theory that, thanks to formal stability guarantees, enhance the safety and robustness of the method. We also provide the practical insights that enable robust deployment of stochastic control using a conventional central processing unit. We deploy the algorithm on a ten-degree-of-freedom mobile manipulator robot. Finally, we open source our generic and multithreaded implementation.

Automated summary

In this article, we explore and utilize sampling-based control techniques to tackle the challenging task of mobile manipulation of articulated objects. Manipulation tasks involve handling non-differentiable switching contact dynamics, posing limitations to traditional gradient-based optimization methods. Sampling-based techniques offer relief to these limitations but do not guarantee the stability and constraints of robots. Therefore, we introduce a novel framework that combines sampling-based control, control barrier functions, and passivity theory to enhance the safety and robustness of robotic manipulation. We also provide practical insights for the robust deployment of stochastic control on a conventional CPU and share our generic and multithreaded implementation as an open-source resource.