Robotic repositioning of human limbs via model predictive control

Abstract

Robots that effectively manipulate the human body could potentially be useful in a wide variety of applications, including assistive applications for people with disabilities. Toward this end, we present a method to enable robots to compliantly manipulate human limbs. Our approach uses model predictive control (MPC). Given an action by the robot, the model predicts how the human body will move and what forces the robot will apply to the human body. The robot uses this model to optimize its actions to achieve desired motions of the human body while controlling applied forces. This optimization is subject to various constraints, including constraints to avoid hyperextension of the human's joints and to avoid slipping of the robot's end effectors. In this paper, our controller uses a quasistatic model of the human limb in contact with the robot's end effectors, which have linear Cartesian stiffness with respect to Cartesian equilibrium positions. We evaluated our approach in simulation with the specific task of lifting the leg of a human body in a supine position (i.e., lying down). In our tests, we varied the goal configuration for the human leg, the stiffness of the robot's two end effectors, and the model error (i.e., the difference between the controller's model of the human body and the actual human body). Our evaluation demonstrates the feasibility of our approach, since our controller performed well in terms of the forces the robot applied to the human leg and the human leg's motions.