Multi-Task Generative Adversarial Nets with Shared Memory for Cross-Domain Coordination Control
This work addresses cross-domain coordination control for collaborative factory automation, offering an incremental improvement in multi-task policy generation.
The paper tackles the challenge of generating sequential decision policies directly from raw sensory data and transferring knowledge across tasks in discrete-time nonlinear systems, using a multi-task generative adversarial network with shared memory. Results on a flexible manufacturing testbed show the model improves task performance with help from related tasks.
Generating sequential decision process from huge amounts of measured process data is a future research direction for collaborative factory automation, making full use of those online or offline process data to directly design flexible make decisions policy, and evaluate performance. The key challenges for the sequential decision process is to online generate sequential decision-making policy directly, and transferring knowledge across tasks domain. Most multi-task policy generating algorithms often suffer from insufficient generating cross-task sharing structure at discrete-time nonlinear systems with applications. This paper proposes the multi-task generative adversarial nets with shared memory for cross-domain coordination control, which can generate sequential decision policy directly from raw sensory input of all of tasks, and online evaluate performance of system actions in discrete-time nonlinear systems. Experiments have been undertaken using a professional flexible manufacturing testbed deployed within a smart factory of Weichai Power in China. Results on three groups of discrete-time nonlinear control tasks show that our proposed model can availably improve the performance of task with the help of other related tasks.