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Shared autonomy allows for combining the global planning capabilities of ahuman operator with the strengths of a robot such as repeatability and accuratecontrol. In a real-time teleoperation setting, one possibility for sharedautonomy is to let the human operator decide for the rough movement and to letthe robot do fine adjustments, e.g., when the view of the operator is occluded.We present a learning-based concept for shared autonomy that aims at supportingthe human operator in a real-time teleoperation setting. At every step, oursystem tracks the target pose set by the human operator as accurately aspossible while at the same time satisfying a set of constraints which influencethe robot's behavior. An important characteristic is that the constraints canbe dynamically activated and deactivated which allows the system to providetask-specific assistance. Since the system must generate robot commands inreal-time, solving an optimization problem in every iteration is not feasible.Instead, we sample potential target configurations and use Neural Networks forpredicting the constraint costs for each configuration. By evaluating eachconfiguration in parallel, our system is able to select the targetconfiguration which satisfies the constraints and has the minimum distance tothe operator's target pose with minimal delay. We evaluate the framework with apick and place task on a bi-manual setup with two Franka Emika Panda robot armswith Robotiq grippers.

Sampling-Based Grasp and Collision Prediction for Assisted Teleoperation