Reactive Planar Manipulation with Convex Hybrid MPC
It addresses real-time control for planar manipulation, but the approach is incremental as it builds on existing MPC and machine learning methods.
This paper tackled the combinatorial complexity in planar manipulation tasks by formulating a convex hybrid MPC program with learned contact modes, achieving good closed-loop performance in trajectory tracking experiments.
This paper presents a reactive controller for planar manipulation tasks that leverages machine learning to achieve real-time performance. The approach is based on a Model Predictive Control (MPC) formulation, where the goal is to find an optimal sequence of robot motions to achieve a desired object motion. Due to the multiple contact modes associated with frictional interactions, the resulting optimization program suffers from combinatorial complexity when tasked with determining the optimal sequence of modes. To overcome this difficulty, we formulate the search for the optimal mode sequences offline, separately from the search for optimal control inputs online. Using tools from machine learning, this leads to a convex hybrid MPC program that can be solved in real-time. We validate our algorithm on a planar manipulation experimental setup where results show that the convex hybrid MPC formulation with learned modes achieves good closed-loop performance on a trajectory tracking problem.