Learning Optimal Impedance Control During Complex 3D Arm Movements

In daily life, humans use their limbs to perform various movements to interact with an external environment. Thanks to limb's variable and adaptive stiffness, humans can adapt their movements to unstable dynamics of the external environments. The underlying adaptive mechanism has been investigated, employing a simple planar device perturbed by external 2D force patterns. In the present work, we will employ a more advanced, compliant robot arm to extend previous work to a more realistic 3D-setting. We study the adaptive mechanism and use machine learning to capture the human adaptation behavior. In order to model human's stiffness adaptive skill, we give human subjects the task to reach for a target by moving a handle assembled on the end-effector of a compliant robotic arm. The arm is force controlled and the human is required to navigate the handle inside a non-visible, virtual maze and explore it only through robot force feedback when contacting maze virtual walls. By sampling the hand's position and force data, a computational model based on a combination of model predictive control and nonlinear regression is used to predict participants' successful trials. Our study shows that participants selectively increased the stiffness within the axis direction of uncertainty in order to compensate for instability caused by a divergent external force field. The learned controller was able to successfully mimic this behavior. When it is deployed on the robot for the navigation task, the robot arm successfully adapt to the unstable dynamics in the virtual maze, in a similar manner as observed in the participants' adaptation skill.