Goal-predictive robotic teleoperation from noisy sensors

Christopher Schultz, Sanket Gaurav, Mathew Monfort, Lingfei Zhang, Brian D. Ziebart

Abstract: Robotic teleoperation from a human operator's pose demonstrations provides an intuitive and effective means of control that has been made feasible by improvements in sensor technologies in recent years. However, the imprecision of low-cost depth cameras and the difficulty of calibrating a frame of reference for the operator introduce inefficiencies in this process when performing tasks that require interactions with objects in the robot's workspace. We develop a goal-predictive teleoperation system that aids in “de-noising” the controls of the operator to be more goal-directed. Our approach uses inverse optimal control to predict the intended object of interaction from the current motion trajectory in real time and then adapts the degree of autonomy between the operator's demonstrations and autonomous completion of the predicted task. We evaluate our approach using the Microsoft Kinect depth camera as our input sensor to control a Rethink Robotics Baxter robot.