Safe Human-Robot Collaborative Transportation via Trust-Driven Role Adaptation

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 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. The robot also uses the sensor feedback to infer obstacles known only by the human and updates its planner to better align with the human’s movements. With experimental results, we demonstrate that our proposed approach increases the success rate of collision-free trials while decreasing the effort required by the human to intervene.