a b s t r a c tIn this paper we present ongoing work on how to incorporate human motion models into the design of a high performance teleoperation platform. A short description of human motion models used for ball-catching is followed by a more detailed study of a teleoperation platform on which to conduct experiments. Also, a pilot study using minimum jerk theory to explain user input behavior in teleoperated catching is presented.

-A teleoperation system for controlling a robot with fast dynamics over the Internet has been constructed. It employs a predictive control structure with an accurate dynamic model of the robot to overcome problems caused by varying delays. The operator interface uses a stereo virtual reality display of the robot cell, and a haptic device for force feed-back including virtual obstacle avoidance forces.

Abstract-The present paper examines minimum jerk models for human kinematics as a tool to predict user input in teleoperation with significant dynamics. Predictions of user input can be a powerful tool to bridge time-delays and to trigger autonomous sub-sequences. In this paper an example implementation is presented, along with the results of a pilot experiment in which a virtual reality simulation of a teleoperated ball-catching scenario is used to test the predictive power of the model. The results show that delays up to 100 ms can potentially be bridged with this approach.