When the dimensionality of control signals issued by a human is smaller than the controllable degree-of-freedom (DoF) of the robot, often the control space is partitioned such that the human operates on a subset of the robot's DoF at a given time (called a control mode). While one way for autonomy to play a role is to blend with the user's commands and bridge the dimensionality gap, another way is to anticipate when to switch between control modes.
This project explores assistance in the form of the robot autonomy anticipating and performing for the user switches between lower dimensional control modes. The exact nature of this assistance is customized to individual users.
Funding Source: National Science Foundation (NSF/CPS-1544797)
In collaboration with Siddhartha Srinivasa, Robotics Institute, Carnegie Mellon University.
© argallab 2016