Multi-Task Reinforcement Learning based Mobile Manipulation Control for Dynamic Object Tracking and Grasping

Understand

Agile control of mobile manipulator is challenging because of the high complexity coupled by the robotic system and the unstructured working environment.

• Tracking and grasping a dynamic object with a random trajectory is even harder.
• In this paper, a multi-task reinforcement learning-based mobile manipulation control framework is proposed to achieve general dynamic object tracking and grasping.
• Several basic types of dynamic trajectories are chosen as the task training set.