Learning assistive teleoperation behaviors from demonstration

Emergency response in hostile environments often involves remotely operated vehicles (ROVs) that are teleoperated as interaction with the environment is typically required. Many ROV tasks are common to such scenarios and are often recurrent. We show how a probabilistic approach can be used to learn a task behavior model from data. Such a model can then be used to assist an operator performing the same task in future missions. We show how this approach can capture behaviors (constraints) that are present in the training data, and how this model can be combined with the operator’s input online. We present an illustrative planar example and elaborate with a non-Destructive testing (NDT) scanning task on a teleoperation mock-up using a two-armed Baxter robot. We demonstrate how our approach can learn from examples task specific behaviors and automatically control the overall system, combining the operator’s input and the learned model online, in an assistive teleoperation manner. This can potentially reduce the time and effort required to perform teleoperation tasks that are commonplace to ROV missions in the context of security, maintenance and rescue robotics.