A Dual-Arm Shared Control Framework Integrating Sub-goals and Predicted Trajectories for Asymmetric Tasks

Abstract

In robotic operation, asymmetric tasks requiring dual-arm cooperation are the highly challenging research direction. Autonomous operation generally has a low success rate or poor generalization because of its excessive dependence on the accuracy of sub-goals from asymmetric tasks. Although teleoperation can significantly improve the performances above, during operation, the operators are prone to neglect crucial intermediate sub-goals that are conducive to fine-grained dual-arm cooperation. Therefore, we propose a dual-arm shared control framework which firstly introduces the Sub-goal Generation module to sufficiently concentrate on the intermediate states, thus improving the ability of fine-grained dual-arm cooperation and reducing the adjustment quantity during robotic asymmetric task operation. Also, we integrate the Trajectory Prediction module that computes the future trajectory based on the historical movement information to enhance the robot motion smoothness. Finally, through dynamic combination of Sub-goal Generation module, Trajectory Prediction module and operator movement in the shared control framework, we effectively decrease the sensitivity to the accuracy of sub-goals, thus significantly improving the success rate. In simulation, we conduct the comparative experiments with autonomous operation and teleoperation on four common asymmetric tasks to validate the advantages of our shared control framework. The effect of each element in our framework is verified by ablation study. Certainly, our shared control framework can also be applied in real-world scenario.