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.
Finally, the complexity of determining the optimal sequence of actions to manipulate an object by pushing is solved offline in REF through machine learning.
Reactive Planar Manipulation with Convex Hybrid MPC
2018 IEEE International Conference on Robotics and Automation (ICRA)
2018 IEEE International Conference on Robotics and Automation (ICRA)
Engineering Computer Science