A Compliant Force Control Strategy for On-Orbit Assembly: Design, Implementation, and Reinforcement Learning-Based Optimization

On-orbit assembly of large-scale space structures is critical for future space missions, yet it poses significant challenges for robotic systems. A fundamental difficulty in tasks like docking, which are highly contact-rich, lies in achieving both high precision and compliant force control to prevent damage. This trade-off between accuracy and safety is difficult to resolve using conventional methods. Furthermore, the development of advanced solutions is constrained by the scarcity of high-fidelity ground testbeds capable of emulating the complex contact dynamics of walking and docking. To address these issues, this paper presents a comprehensive system-level solution. First, a novel seven-degree-of-freedom walking and assembly integrated robotic platform, along with a high-load-bearing docking interface, is developed to facilitate practical verification. Second, based on this platform, a novel hybrid compliant control framework is proposed, which integrates a low-level impedance controller with a high-level Model-Based Reinforcement Learning (MBRL) planner. Unlike passive compliance schemes, the MBRL agent leverages a learned dynamics model to predict interaction forces and proactively generate corrective force trajectory commands. This hierarchical architecture allows the system to accommodate geometric uncertainties and maintain continuous contact without triggering excessive force spikes. Experimental results validate the effectiveness of the proposed approach. Compared to traditional impedance control, the hybrid control strategy significantly reduces contact forces during docking while achieving a final high assembly accuracy. This work demonstrates a viable system-level solution, from hardware design to intelligent control, for advancing robotic on-orbit assembly.