Two-Stage Motion Strategy Transfer for Robots Across Simulation and Physical Domains Based on Reinforcement Learning

Training robots in real-world scenarios to obtain intelligent control strategies often requires a significant amount of training costs. To address this, we present a two-stage cross-domain consistency constraint motion strategy transfer algorithm that bridges the gap between simulated and real-world robotics. The approach consists of two stages. In first stage, using the BTD3 reinforcement learning algorithm to train motion control strategies in simulation, and applying supervised learning on historical real-world data to develop the state inference model. The second stage involves creating cross-domain state and action mapping models with cross-domain consistency constraint to ensure effective transfer of simulated strategies to real robots. Experimental results show that our algorithm is highly effective in transferring control strategies from one agent to a similar agent, providing a cost-effective solution for obtaining intelligent control strategies for real-world robots.