Ultrafast Acoustic Holography with Physics-Reinforced Self-Supervised Learning for Precise Robotic Manipulation

Ultrafast acoustic holography (AH) enabling dynamic contactless micro-nano robotic manipulation has recently attracted wide attention. As an advanced technique, AH encodes specific three-dimensional (3D) acoustic field on a two-dimensional (2D) hologram whereby realizing holographic reconstruction with high fidelity. However, current approaches face the limitation of encoding time, accuracy and flexibility, thus, leading to inapplicability for dynamic and precise robotic manipulation. Here, we develop an approach to overcome these issues. Its basic idea is to use a convolutional neural network trained in a self-supervised manner with iterative interaction with virtual physical environment. Energy conservation is incorporated to access the physical constrain during wave propagation. The experimental results demonstrate that the proposed method circumvents laborious annotated dataset preparation and boosts the reinforcement from physics model. By the validation and comparison on distinct acoustic fields with various patterns, the accuracy and real-time performance of the proposed method are confirmed supporting dynamic and precise robotic manipulation.