  Shared autonomy provides a framework where a human and an automated system,
such as a robot, jointly control the system's behavior, enabling an effective
solution for various applications, including human-robot interaction. However,
a challenging problem in shared autonomy is safety because the human input may
be unknown and unpredictable, which affects the robot's safety constraints. If
the human input is a force applied through physical contact with the robot, it
also alters the robot's behavior to maintain safety. We address the safety
issue of shared autonomy in real-time applications by proposing a two-layer
control framework. In the first layer, we use the history of human input
measurements to infer what the human wants the robot to do and define the
robot's safety constraints according to that inference. In the second layer, we
formulate a rapidly-exploring random tree of barrier pairs, with each barrier
pair composed of a barrier function and a controller. Using the controllers in
these barrier pairs, the robot is able to maintain its safe operation under the
intervention from the human input. This proposed control framework allows the
robot to assist the human while preventing them from encountering safety
issues. We demonstrate the proposed control framework on a simulation of a
two-linkage manipulator robot.
