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Abstract
In teleoperation, the operator is often required to command the motion of the remote robot and monitor its behavior. However, such an interaction demands a heavy workload from a human operator when facing with complex tasks and dynamic environments. In this article, we propose a shared control method to assist the operator in the manipulation tasks to reduce the workload and improve the efficiency. We adopt a task-parameterized hidden semi-Markov model to learn a manipulation skill from several human demonstrations. We utilize the learned model to predict the manipulation target given the current observed robotic motion trajectory and subsequently estimate the desired robotic motion given the current input of the operator. The estimated robotic motion is then utilized to correct the input of the operator to provide manipulation assistance. In addition, a set of virtual reality devices are used to capture the operator’s motion and display the vision feedback from the remote site. We evaluate our approach through two manipulation tasks with a dual-arm robot. The experimental results show the effectiveness of the proposed method.
Highlights
Advancements in robot learning theories, such as deep reinforcement learning[1,2] and imitation learning,[3] enable robots carry out many tasks independently, but human intervention is still indispensable for many complex tasks, such as handling hazardous materials, underwater and space exploration, minimally invasive surgery, and so on
A human operator is directly required to control the remote robot in detail while monitoring its behavior, which results in a heavy workload.[4]
We propose a shared control method based on task-parameterized hidden semi-Markov model (TP-HSMM).[7]
Summary
Advancements in robot learning theories, such as deep reinforcement learning[1,2] and imitation learning,[3] enable robots carry out many tasks independently, but human intervention is still indispensable for many complex tasks, such as handling hazardous materials, underwater and space exploration, minimally invasive surgery, and so on. Teleoperating a dual-arm robot to accomplish such cooperative tasks is a complicated skill In such scenarios, shared control methods can reduce the workload and improve operation efficiency by combining manual teleoperation with autonomous assistance. We propose a shared control method based on task-parameterized hidden semi-Markov model (TP-HSMM).[7] The task parameters here refer to the variables that describe the manipulation context, such as the positions of some task-related objects, which can be used to reshape the robotic movement. The contributions of the proposed method are as follows: (i) we adopt TP-HSMM to learn a robotic manipulation skill so that our shared control method can adapt to new tasks rapidly. Conclusions and future work are presented in the fifth section
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