A learning-based shared control architecture for interactive task execution

Shared control is a key technology for various robotic applications in which a robotic system and a human operator are meant to collaborate efficiently. In order to achieve efficient task execution in shared control, it is essential to predict the desired behavior for a given situation or context in order to simplify the control task for the human operator. This prediction is obtained by exploiting Learning from Demonstration (LfD), which is a popular approach for transferring human skills to robots. We encode the demonstrated behavior as trajectory distributions and generalize the learned distributions to new situations. The goal of this paper is to present a shared control framework that uses learned expert distributions to gain more autonomy. Our approach controls the balance between the controller's autonomy and the human preference based on the distributions of the demonstrated trajectories. Moreover, the learned distributions are autonomously refined from collaborative task executions, resulting in a master-slave system with increasing autonomy that requires less user input with an increasing number of task executions. We experimentally validated that our shared control approach enables efficient task executions. Moreover, the conducted experiments demonstrated that the developed system improves its performances through interactive task executions with our shared control.

Authors

• Firas Abi-Farraj
• Takayuki Osa
• Nicolò Pedemonte
• Jan Peters 0001
• Gerhard Neumann
• Paolo Robuffo Giordano

Keywords

• Trajectory
• Manipulators
• Force feedback
• Adaptation models
• Force
• Task Execution
• Shared Control
• Shared Control Architecture
• Robotic System
• Human Operator
• Context In Order
• Human Preferences
• Distribution Of Trajectories
• Inverse Reinforcement Learning
• Shared Framework
• Conditional Distribution
• Kullback-Leibler
• Information Gain
• Target Object
• Stiffness Matrix
• Learning Phase
• Human Experts
• Object Position
• End-effector
• Iterative Experiments
• Assistance Systems
• Context Vector
• Human Input
• Unit Quaternion
• Robot Motion
• Teleoperation System
• Diagonal Elements Of Matrix
• Velocity Commands