Teleoperation Experience Like VR Games: Generating Object-Grasping Motions Based on Predictive Learning

Teleoperation is popular due to its several advantages, including the ability to control a robot from a distance and the capacity for the operator to manage the robot safely. However, teleoperation also presents challenges, including operational complexity and the requirement for a certain level of proficiency from the operator. For instance, when attempting to grasp an object via teleoperation, issues such as communication delays, inadequate feedback from the robot to the operator, and the complexity of the grasping trajectory can arise. To address this issue, we propose an intuitive teleoperation method that facilitates data collection using VR devices and a technique for generating object-grasping motions through predictive learning with the collected data. First, we collect the motion data while the robot is teleoperated using a VR device. The collected motion data is used to create a predictive model through predictive learning, which in turn is used to generate object-grasping motions. This approach allows us to collect motion data suitable for machine learning while performing intuitive teleoperation. It also enables the generation of object-grasping motions with simple operations, making robot teleoperation experience similar to a VR game. We evaluated our approach's ability to generate object-grasping motion with predictive model. The results show that our approach can generate object-grasping motions with a certain level of success. In light of our results, we discussed the factors that pose challenges to predictive learning and explored the future prospects of this approach.