  Dual-arm robots have great application prospects in intelligent manufacturing
due to their human-like structure when deployed with advanced intelligence
algorithm. However, the previous visuomotor policy suffers from perception
deficiencies in environments where features of images are impaired by the
various conditions, such as abnormal lighting, occlusion and shadow etc. The
Focal CVAE framework is proposed for RGB-D multi-modal data fusion to address
this challenge. In this study, a mixed focal attention module is designed for
the fusion of RGB images containing color features and depth images containing
3D shape and structure information. This module highlights the prominent local
features and focuses on the relevance of RGB and depth via cross-attention. A
saliency attention module is proposed to improve its computational efficiency,
which is applied in the encoder and the decoder of the framework. We illustrate
the effectiveness of the proposed method via extensive simulation and
experiments. It's shown that the performances of bi-manipulation are all
significantly improved in the four real-world tasks with lower computational
cost. Besides, the robustness is validated through experiments under different
scenarios where there is a perception deficiency problem, demonstrating the
feasibility of the method.
