.png){kind=icon}
.png){kind=icon}
Return
A Novel Simulation-Reality Closed-Loop Learning Framework for Autonomous Robot Skill Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TCDS.2021.3118294.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In recent years, data-driven learning methods have been widely studied for autonomous robot skill learning. However, these methods rely on large amounts of robot-environment interaction data for training, which largely prevents them from being applied to real-world robots. To address this problem, this article proposes a novel simulation-reality closed-loop learning framework for autonomous robot skill learning that can improve data efficiency, enhance policy stability, and achieve effective policy simulation-to-reality (sim2real) transfer. First, a hybrid control model combining the asymmetric deep deterministic policy gradients (Asym-DDPGs) model and the forward prediction control (FPC) model is proposed to learn vision-based manipulation policies in simulations, which can decompose complex tasks to improve learning efficiency. Second, a novel pixel-level domain adaptation method named Position-CycleGAN is designed to translate real images to simulated images while also preserving the task-related information. The policy trained in simulations can be directly migrated into real robots in a reverse reality-to-simulation manner using the Position-CycleGAN model. The experimental results validate the effectiveness of the proposed framework. This work provides an efficient and feasible path for achieving autonomous skill learning.
Keywords:
Autonomous robot skill learning
hybrid control model
pixel-level domain adaptation
simulation-reality closed-loop
Journal
IF:
4.9
Papers:
1.0K
Citations:
3.5K