Interface-level Intent Inference for Environment-agnostic Robot Teleoperation Assistance

In robot teleoperation, humans issue control signals through interfaces that require physical actuation.This interfacelevel interaction largely goes unmodeled within the field, yet the interpretation of an interface-level command can differ from what was intended by the user, as a result of diminished human ability or inadequate mappings from raw interface signals to robot control signals.Interface-aware systems aim to address this limitation in robot teleoperation by explicitly considering the impact of a control interface on user input quality when interpreting interface signals for robot control.This work presents an interface-aware formulation for the direct inference of intended interface-level commands given known interaction characteristics of a control interface using data-driven modeling, allowing for teleoperation assistance without knowledge of the human's policy.In our specific implementation, we tailor the formulation to model a user's operation of a sip/puff interface using a network of Gated Recurrent Units, chosen for their ability to model temporal patterns and suitability for data-scarce domains.The resulting model is agnostic to the robot being controlled, which allows for its use in task-and environment-agnostic robot teleoperation assistance.We deploy this model in two variations of assisted teleoperation frameworks using a 1-D sip/puff interface to control a 7-DoF robotic arm, and conduct a human subjects study with spinal cord injured participants to evaluate the efficacy of our method.Our proposed task-and environment-agnostic formulation is effective in reducing collisions during teleoperation, and is preferred by users over teleoperation baselines for ease and intuitiveness of robot operation.