The application discloses an intelligent virtual human assisted maintenance method and
system based on SAC
algorithm and FSM, and belongs to the virtual human
simulation field. The application combines deep
reinforcement learning and
finite state machine to design the autonomous maintenance behavior of the virtual human. The simple actions such as walking, running, standing and turning of the virtual human are controlled by using the state
machine, and the complex actions such as maintenance are trained by using the deep
reinforcement learning. Compared with the traditional virtual human assisted maintenance design, the application avoids excessive human intervention, reduces the
system running memory consumption, effectively improves the maintenance efficiency, makes the virtual human have the fidelity, and effectively avoids the shortcomings of the state
machine and the behavior tree method. The virtual
human visual perception model based on the multi-Cube trigger is used, and the problems of the scene unable to be deeply optimized and the missed objects caused by the
ray detection are avoided. The application makes the virtual human more intelligent, helps to enhance the reality and credibility of the virtual human, and improves the creativity, immersion and real-time interactivity of the
virtual maintenance operation.