  We study a human-robot collaborative transportation task in presence of
obstacles. The task for each agent is to carry a rigid object to a common
target position, while safely avoiding obstacles and satisfying the compliance
and actuation constraints of the other agent. Human and robot do not share the
local view of the environment. The human policy either assists the robot when
they deem the robot actions safe based on their perception of the environment,
or actively leads the task. Using estimated human inputs, the robot plans a
trajectory for the transported object by solving a constrained finite time
optimal control problem. Sensors on the robot measure the inputs applied by the
human. The robot then appropriately applies a weighted combination of the
human's applied and its own planned inputs, where the weights are chosen based
on the robot's trust value on its estimates of the human's inputs. This allows
for a dynamic leader-follower role adaptation of the robot throughout the task.
Furthermore, under a low value of trust, if the robot approaches any obstacle
potentially unknown to the human, it triggers a safe stopping policy,
maintaining safety of the system and signaling a required change in the human's
intent. With experimental results, we demonstrate the efficacy of the proposed
approach.
