A framework for efficient teleoperation via online adaptation

We propose a task-independent adaptive teleoperation methodology that seeks to improve operator performance and efficiency by concurrently modeling user intent and adapting the set of available actions according to the predicted intent. User input selects a robot motion from a finite set of dynamically feasible and safe motions, represented as a motion primitive library. User intent is modeled as a probabilistic distribution with respect to future actions that represents the likelihood of action selection given recent user input, which can be formulated independent of task, environment, or user. As the intent model becomes increasingly confident, the action set is adapted in order to reduce the error between the intended and actual performance. Experimental evaluation of teleoperating a quadrotor for nonaggressive, single-intent maneuvers such as following a racetrack and conducting a free-hand helix motion shows improved performance, validating that the approach provides efficient adaptation towards achieving the user intent.